QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wubin Pan [verfasserIn] Guang Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 25142-25156
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:25142-25156

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2018.2811416

Katalog-ID:	DOAJ068441282

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ068441282
003	DE-627
005	20230502064501.0
007	cr uuu---uuuuu
008	230228s2018 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2018.2811416 \|2 doi
035			\|a (DE-627)DOAJ068441282
035			\|a (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Wubin Pan \|e verfasserin \|4 aut
245	1	0	\|a QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
264		1	\|c 2018
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.
650		4	\|a Hyper text transfer protocol over secure socket layer (HTTPS) YouTube
650		4	\|a QoE assessment
650		4	\|a adaptive streaming
650		4	\|a machine learning
650		4	\|a smart city
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Guang Cheng \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 6(2018), Seite 25142-25156 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:6 \|g year:2018 \|g pages:25142-25156
856	4	0	\|u https://doi.org/10.1109/ACCESS.2018.2811416 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/8310894/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 6 \|j 2018 \|h 25142-25156

Indexfelder

author_variant	w p wp g c gc
matchkey_str	article:21693536:2018----::oassmnoecytdotbaatvsraigoeeg
hierarchy_sort_str	2018
callnumber-subject-code	TK
publishDate	2018
allfields	10.1109/ACCESS.2018.2811416 doi (DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b DE-627 ger DE-627 rakwb eng TK1-9971 Wubin Pan verfasserin aut QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering Guang Cheng verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25142-25156 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25142-25156 https://doi.org/10.1109/ACCESS.2018.2811416 kostenfrei https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b kostenfrei https://ieeexplore.ieee.org/document/8310894/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25142-25156
spelling	10.1109/ACCESS.2018.2811416 doi (DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b DE-627 ger DE-627 rakwb eng TK1-9971 Wubin Pan verfasserin aut QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering Guang Cheng verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25142-25156 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25142-25156 https://doi.org/10.1109/ACCESS.2018.2811416 kostenfrei https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b kostenfrei https://ieeexplore.ieee.org/document/8310894/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25142-25156
allfields_unstemmed	10.1109/ACCESS.2018.2811416 doi (DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b DE-627 ger DE-627 rakwb eng TK1-9971 Wubin Pan verfasserin aut QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering Guang Cheng verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25142-25156 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25142-25156 https://doi.org/10.1109/ACCESS.2018.2811416 kostenfrei https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b kostenfrei https://ieeexplore.ieee.org/document/8310894/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25142-25156
allfieldsGer	10.1109/ACCESS.2018.2811416 doi (DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b DE-627 ger DE-627 rakwb eng TK1-9971 Wubin Pan verfasserin aut QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering Guang Cheng verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25142-25156 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25142-25156 https://doi.org/10.1109/ACCESS.2018.2811416 kostenfrei https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b kostenfrei https://ieeexplore.ieee.org/document/8310894/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25142-25156
allfieldsSound	10.1109/ACCESS.2018.2811416 doi (DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b DE-627 ger DE-627 rakwb eng TK1-9971 Wubin Pan verfasserin aut QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering Guang Cheng verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 25142-25156 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:25142-25156 https://doi.org/10.1109/ACCESS.2018.2811416 kostenfrei https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b kostenfrei https://ieeexplore.ieee.org/document/8310894/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 25142-25156
language	English
source	In IEEE Access 6(2018), Seite 25142-25156 volume:6 year:2018 pages:25142-25156
sourceStr	In IEEE Access 6(2018), Seite 25142-25156 volume:6 year:2018 pages:25142-25156
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Wubin Pan @@aut@@ Guang Cheng @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ068441282
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ068441282</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502064501.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2018.2811416</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ068441282</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Wubin Pan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyper text transfer protocol over secure socket layer (HTTPS) YouTube</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QoE assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive streaming</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">smart city</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guang Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">6(2018), Seite 25142-25156</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:25142-25156</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2018.2811416</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8310894/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="h">25142-25156</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Wubin Pan
spellingShingle	Wubin Pan misc TK1-9971 misc Hyper text transfer protocol over secure socket layer (HTTPS) YouTube misc QoE assessment misc adaptive streaming misc machine learning misc smart city misc Electrical engineering. Electronics. Nuclear engineering QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
authorStr	Wubin Pan
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities Hyper text transfer protocol over secure socket layer (HTTPS) YouTube QoE assessment adaptive streaming machine learning smart city
topic	misc TK1-9971 misc Hyper text transfer protocol over secure socket layer (HTTPS) YouTube misc QoE assessment misc adaptive streaming misc machine learning misc smart city misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Hyper text transfer protocol over secure socket layer (HTTPS) YouTube misc QoE assessment misc adaptive streaming misc machine learning misc smart city misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Hyper text transfer protocol over secure socket layer (HTTPS) YouTube misc QoE assessment misc adaptive streaming misc machine learning misc smart city misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
ctrlnum	(DE-627)DOAJ068441282 (DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b
title_full	QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
author_sort	Wubin Pan
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2018
contenttype_str_mv	txt
container_start_page	25142
author_browse	Wubin Pan Guang Cheng
container_volume	6
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Wubin Pan
doi_str_mv	10.1109/ACCESS.2018.2811416
author2-role	verfasserin
title_sort	qoe assessment of encrypted youtube adaptive streaming for energy saving in smart cities
callnumber	TK1-9971
title_auth	QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
abstract	Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.
abstractGer	Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.
abstract_unstemmed	Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities
url	https://doi.org/10.1109/ACCESS.2018.2811416 https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b https://ieeexplore.ieee.org/document/8310894/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Guang Cheng
author2Str	Guang Cheng
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2018.2811416
callnumber-a	TK1-9971
up_date	2024-07-03T17:47:56.210Z
_version_	1803580998899728384
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ068441282</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502064501.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2018.2811416</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ068441282</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd38b039c6e254143b80d91501cfe0a0b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Wubin Pan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyper text transfer protocol over secure socket layer (HTTPS) YouTube</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QoE assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive streaming</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">smart city</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Guang Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">6(2018), Seite 25142-25156</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:25142-25156</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2018.2811416</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d38b039c6e254143b80d91501cfe0a0b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8310894/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2018</subfield><subfield code="h">25142-25156</subfield></datafield></record></collection>
score	7.4008837

Nicht das Richtige dabei?

Schreiben Sie uns!

QoE Assessment of Encrypted YouTube Adaptive Streaming for Energy Saving in Smart Cities

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?