Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-12
Language
English
Article Type
Article
Keywords

WIRELESS NETWORKS; SCALABLE VIDEO; ALLOCATION; MEDIA

Citation

IEEE TRANSACTIONS ON MULTIMEDIA, v.18, no.12, pp.2517 - 2527

ISSN
1520-9210
DOI
10.1109/TMM.2016.2604565
URI
http://hdl.handle.net/10203/214210
Appears in Collection
EE-Journal Papers(저널논문)
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