Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/655797
Title: Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection
Authors: Joohyun Lee;Kyunghan Lee;Choongwoo Han;Taehoon Kim;Song Chong
subject: resource efficiency|Markov decision process|mobile video streaming|Communication energy saving
Year: 2016
Publisher: IEEE
Abstract: From the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.
URI: http://localhost/handle/Hannan/160266
http://localhost/handle/Hannan/655797
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

Files in This Item:
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Title: Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection
Authors: Joohyun Lee;Kyunghan Lee;Choongwoo Han;Taehoon Kim;Song Chong
subject: resource efficiency|Markov decision process|mobile video streaming|Communication energy saving
Year: 2016
Publisher: IEEE
Abstract: From the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.
URI: http://localhost/handle/Hannan/160266
http://localhost/handle/Hannan/655797
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7556972.pdf1.45 MBAdobe PDFThumbnail
Preview File
Title: Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection
Authors: Joohyun Lee;Kyunghan Lee;Choongwoo Han;Taehoon Kim;Song Chong
subject: resource efficiency|Markov decision process|mobile video streaming|Communication energy saving
Year: 2016
Publisher: IEEE
Abstract: From the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.
URI: http://localhost/handle/Hannan/160266
http://localhost/handle/Hannan/655797
ISSN: 1520-9210
1941-0077
volume: 18
issue: 12
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7556972.pdf1.45 MBAdobe PDFThumbnail
Preview File