Context-Aware Interflow Network Coding and Scheduling in Wireless Networks

Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presenc...
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Autor*in:	Tran, Tuan [verfasserIn] Nguyen, Thinh

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers

Übergeordnetes Werk:	Enthalten in: IEEE transactions on vehicular technology - New York, NY : IEEE, 1967, 65(2016), 11, Seite 9299-9318
Übergeordnetes Werk:	volume:65 ; year:2016 ; number:11 ; pages:9299-9318

Links:	Volltext Link aufrufen

DOI / URN:	10.1109/TVT.2016.2520361

Katalog-ID:	OLC1984346172

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520			\|a Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques.
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allfields	10.1109/TVT.2016.2520361 doi PQ20161201 (DE-627)OLC1984346172 (DE-599)GBVOLC1984346172 (PRQ)c991-da6defe98ebb5f168adbb5063debc26c9f5733637255ec64095ddfee1f9db5d90 (KEY)0030991520160000065001109299contextawareinterflownetworkcodingandschedulinginw DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Tran, Tuan verfasserin aut Context-Aware Interflow Network Coding and Scheduling in Wireless Networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques. random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers Nguyen, Thinh oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 11, Seite 9299-9318 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:11 pages:9299-9318 http://dx.doi.org/10.1109/TVT.2016.2520361 Volltext http://ieeexplore.ieee.org/document/7389421 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 11 9299-9318
spelling	10.1109/TVT.2016.2520361 doi PQ20161201 (DE-627)OLC1984346172 (DE-599)GBVOLC1984346172 (PRQ)c991-da6defe98ebb5f168adbb5063debc26c9f5733637255ec64095ddfee1f9db5d90 (KEY)0030991520160000065001109299contextawareinterflownetworkcodingandschedulinginw DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Tran, Tuan verfasserin aut Context-Aware Interflow Network Coding and Scheduling in Wireless Networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques. random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers Nguyen, Thinh oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 11, Seite 9299-9318 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:11 pages:9299-9318 http://dx.doi.org/10.1109/TVT.2016.2520361 Volltext http://ieeexplore.ieee.org/document/7389421 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 11 9299-9318
allfields_unstemmed	10.1109/TVT.2016.2520361 doi PQ20161201 (DE-627)OLC1984346172 (DE-599)GBVOLC1984346172 (PRQ)c991-da6defe98ebb5f168adbb5063debc26c9f5733637255ec64095ddfee1f9db5d90 (KEY)0030991520160000065001109299contextawareinterflownetworkcodingandschedulinginw DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Tran, Tuan verfasserin aut Context-Aware Interflow Network Coding and Scheduling in Wireless Networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques. random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers Nguyen, Thinh oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 11, Seite 9299-9318 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:11 pages:9299-9318 http://dx.doi.org/10.1109/TVT.2016.2520361 Volltext http://ieeexplore.ieee.org/document/7389421 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 11 9299-9318
allfieldsGer	10.1109/TVT.2016.2520361 doi PQ20161201 (DE-627)OLC1984346172 (DE-599)GBVOLC1984346172 (PRQ)c991-da6defe98ebb5f168adbb5063debc26c9f5733637255ec64095ddfee1f9db5d90 (KEY)0030991520160000065001109299contextawareinterflownetworkcodingandschedulinginw DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Tran, Tuan verfasserin aut Context-Aware Interflow Network Coding and Scheduling in Wireless Networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques. random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers Nguyen, Thinh oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 11, Seite 9299-9318 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:11 pages:9299-9318 http://dx.doi.org/10.1109/TVT.2016.2520361 Volltext http://ieeexplore.ieee.org/document/7389421 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 11 9299-9318
allfieldsSound	10.1109/TVT.2016.2520361 doi PQ20161201 (DE-627)OLC1984346172 (DE-599)GBVOLC1984346172 (PRQ)c991-da6defe98ebb5f168adbb5063debc26c9f5733637255ec64095ddfee1f9db5d90 (KEY)0030991520160000065001109299contextawareinterflownetworkcodingandschedulinginw DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Tran, Tuan verfasserin aut Context-Aware Interflow Network Coding and Scheduling in Wireless Networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques. random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers Nguyen, Thinh oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 11, Seite 9299-9318 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:11 pages:9299-9318 http://dx.doi.org/10.1109/TVT.2016.2520361 Volltext http://ieeexplore.ieee.org/document/7389421 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 11 9299-9318
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author	Tran, Tuan
spellingShingle	Tran, Tuan ddc 620 bkl 53.70 bkl 53.74 misc random network coding (RNC) misc Encoding misc Streaming media misc Network coding misc Quality of service misc quality of service (QoS) misc Multiuser multiservice scheduling misc Throughput misc Wireless networks misc Receivers Context-Aware Interflow Network Coding and Scheduling in Wireless Networks
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topic_title	620 DNB 53.70 bkl 53.74 bkl Context-Aware Interflow Network Coding and Scheduling in Wireless Networks random network coding (RNC) Encoding Streaming media Network coding Quality of service quality of service (QoS) Multiuser multiservice scheduling Throughput Wireless networks Receivers
topic	ddc 620 bkl 53.70 bkl 53.74 misc random network coding (RNC) misc Encoding misc Streaming media misc Network coding misc Quality of service misc quality of service (QoS) misc Multiuser multiservice scheduling misc Throughput misc Wireless networks misc Receivers
topic_unstemmed	ddc 620 bkl 53.70 bkl 53.74 misc random network coding (RNC) misc Encoding misc Streaming media misc Network coding misc Quality of service misc quality of service (QoS) misc Multiuser multiservice scheduling misc Throughput misc Wireless networks misc Receivers
topic_browse	ddc 620 bkl 53.70 bkl 53.74 misc random network coding (RNC) misc Encoding misc Streaming media misc Network coding misc Quality of service misc quality of service (QoS) misc Multiuser multiservice scheduling misc Throughput misc Wireless networks misc Receivers
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title	Context-Aware Interflow Network Coding and Scheduling in Wireless Networks
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title_full	Context-Aware Interflow Network Coding and Scheduling in Wireless Networks
author_sort	Tran, Tuan
journal	IEEE transactions on vehicular technology
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title_sort	context-aware interflow network coding and scheduling in wireless networks
title_auth	Context-Aware Interflow Network Coding and Scheduling in Wireless Networks
abstract	Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques.
abstractGer	Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques.
abstract_unstemmed	Recent approaches using network coding (NC) to mix data from different flows show significant throughput improvement in wireless networks. However, in this paper, we argue that exhaustively mixing packets from different flows may decrease network quality of service (QoS), particularly in the presence of flows with different service classes. We therefore propose a context-aware interflow network coding and scheduling (CARE) framework, which adaptively encodes data across the traffic to maximize the network QoS. First, we develop a perception-oriented QoS (PQoS) to measure the user satisfaction of different types of services. Next, based on the characteristics of the traffic, we optimally combine data across the flows and schedule the encoded packets in each time frame to maximize the PQoS at the receivers. Solving CARE is NP-hard; thus, we devise a computationally efficient approximation algorithm based on the Markov chain Monte Carlo method to approximate the optimal solution. We prove that the proposed approximation algorithm is guaranteed to converge to the optimal solution. The analytical and simulation results show that, under certain channel conditions, the proposed CARE-based schemes not only improve the network QoS but achieve high throughput across all receivers as well. Additionally, the results show that the approximation algorithm is efficient and robust to the number of data flows. In some transmission conditions, our CARE-based schemes can improve the network QoS up to 50% compared with the existing randomized NC techniques.
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title_short	Context-Aware Interflow Network Coding and Scheduling in Wireless Networks
url	http://dx.doi.org/10.1109/TVT.2016.2520361 http://ieeexplore.ieee.org/document/7389421
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doi_str	10.1109/TVT.2016.2520361
up_date	2024-07-04T00:05:39.899Z
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