Towards secure and network state aware bitrate adaptation at IoT edge

Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. The...
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Autor*in:	Zeng, Zeng [verfasserIn] Che, Hang [verfasserIn] Miao, Weiwei [verfasserIn] Huang, Jin [verfasserIn] Tang, Hao [verfasserIn] Zhang, Mingxuan [verfasserIn] Zhang, Shaqian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Adaptive bitrate algorithm IoT

Übergeordnetes Werk:	Enthalten in: Journal of Cloud Computing - Berlin : SpringerOpen, 2012, 9(2020), 1 vom: 13. Juli
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:1 ; day:13 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s13677-020-00189-4

Katalog-ID:	SPR040327620

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520			\|a Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
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allfields	10.1186/s13677-020-00189-4 doi (DE-627)SPR040327620 (SPR)s13677-020-00189-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Zeng, Zeng verfasserin aut Towards secure and network state aware bitrate adaptation at IoT edge 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments. Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Che, Hang verfasserin aut Miao, Weiwei verfasserin aut Huang, Jin verfasserin aut Tang, Hao verfasserin aut Zhang, Mingxuan verfasserin aut Zhang, Shaqian verfasserin aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 9(2020), 1 vom: 13. Juli (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:9 year:2020 number:1 day:13 month:07 https://dx.doi.org/10.1186/s13677-020-00189-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.32 ASE 54.70 ASE 85.20 ASE AR 9 2020 1 13 07
spelling	10.1186/s13677-020-00189-4 doi (DE-627)SPR040327620 (SPR)s13677-020-00189-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Zeng, Zeng verfasserin aut Towards secure and network state aware bitrate adaptation at IoT edge 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments. Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Che, Hang verfasserin aut Miao, Weiwei verfasserin aut Huang, Jin verfasserin aut Tang, Hao verfasserin aut Zhang, Mingxuan verfasserin aut Zhang, Shaqian verfasserin aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 9(2020), 1 vom: 13. Juli (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:9 year:2020 number:1 day:13 month:07 https://dx.doi.org/10.1186/s13677-020-00189-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.32 ASE 54.70 ASE 85.20 ASE AR 9 2020 1 13 07
allfields_unstemmed	10.1186/s13677-020-00189-4 doi (DE-627)SPR040327620 (SPR)s13677-020-00189-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Zeng, Zeng verfasserin aut Towards secure and network state aware bitrate adaptation at IoT edge 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments. Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Che, Hang verfasserin aut Miao, Weiwei verfasserin aut Huang, Jin verfasserin aut Tang, Hao verfasserin aut Zhang, Mingxuan verfasserin aut Zhang, Shaqian verfasserin aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 9(2020), 1 vom: 13. Juli (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:9 year:2020 number:1 day:13 month:07 https://dx.doi.org/10.1186/s13677-020-00189-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.32 ASE 54.70 ASE 85.20 ASE AR 9 2020 1 13 07
allfieldsGer	10.1186/s13677-020-00189-4 doi (DE-627)SPR040327620 (SPR)s13677-020-00189-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Zeng, Zeng verfasserin aut Towards secure and network state aware bitrate adaptation at IoT edge 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments. Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Che, Hang verfasserin aut Miao, Weiwei verfasserin aut Huang, Jin verfasserin aut Tang, Hao verfasserin aut Zhang, Mingxuan verfasserin aut Zhang, Shaqian verfasserin aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 9(2020), 1 vom: 13. Juli (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:9 year:2020 number:1 day:13 month:07 https://dx.doi.org/10.1186/s13677-020-00189-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.32 ASE 54.70 ASE 85.20 ASE AR 9 2020 1 13 07
allfieldsSound	10.1186/s13677-020-00189-4 doi (DE-627)SPR040327620 (SPR)s13677-020-00189-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Zeng, Zeng verfasserin aut Towards secure and network state aware bitrate adaptation at IoT edge 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments. Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213 Che, Hang verfasserin aut Miao, Weiwei verfasserin aut Huang, Jin verfasserin aut Tang, Hao verfasserin aut Zhang, Mingxuan verfasserin aut Zhang, Shaqian verfasserin aut Enthalten in Journal of Cloud Computing Berlin : SpringerOpen, 2012 9(2020), 1 vom: 13. Juli (DE-627)726491810 (DE-600)2682472-3 2192-113X nnns volume:9 year:2020 number:1 day:13 month:07 https://dx.doi.org/10.1186/s13677-020-00189-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.32 ASE 54.70 ASE 85.20 ASE AR 9 2020 1 13 07
language	English
source	Enthalten in Journal of Cloud Computing 9(2020), 1 vom: 13. Juli volume:9 year:2020 number:1 day:13 month:07
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topic_facet	Adaptive bitrate algorithm IoT
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authorswithroles_txt_mv	Zeng, Zeng @@aut@@ Che, Hang @@aut@@ Miao, Weiwei @@aut@@ Huang, Jin @@aut@@ Tang, Hao @@aut@@ Zhang, Mingxuan @@aut@@ Zhang, Shaqian @@aut@@
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author	Zeng, Zeng
spellingShingle	Zeng, Zeng ddc 004 bkl 54.32 bkl 54.70 bkl 85.20 misc Adaptive bitrate algorithm misc IoT Towards secure and network state aware bitrate adaptation at IoT edge
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topic_title	004 ASE 54.32 bkl 54.70 bkl 85.20 bkl Towards secure and network state aware bitrate adaptation at IoT edge Adaptive bitrate algorithm (dpeaa)DE-He213 IoT (dpeaa)DE-He213
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title	Towards secure and network state aware bitrate adaptation at IoT edge
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title_full	Towards secure and network state aware bitrate adaptation at IoT edge
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title_sort	towards secure and network state aware bitrate adaptation at iot edge
title_auth	Towards secure and network state aware bitrate adaptation at IoT edge
abstract	Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
abstractGer	Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
abstract_unstemmed	Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
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title_short	Towards secure and network state aware bitrate adaptation at IoT edge
url	https://dx.doi.org/10.1186/s13677-020-00189-4
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author2	Che, Hang Miao, Weiwei Huang, Jin Tang, Hao Zhang, Mingxuan Zhang, Shaqian
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up_date	2024-07-03T15:16:09.515Z
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In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. 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