Abstract | ||
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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. |
Year | DOI | Venue |
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2016 | 10.1109/TMM.2016.2604565 | IEEE Trans. Multimedia |
Keywords | Field | DocType |
Streaming media,Mobile communication,YouTube,Mobile computing,IEEE 802.11 Standard,Energy consumption,Heuristic algorithms | Mobile computing,Wireless network,Job shop scheduling,Computer science,Heuristic (computer science),Scheduling (computing),Markov decision process,Computer network,Energy consumption,Mobile telephony,Distributed computing | Journal |
Volume | Issue | ISSN |
18 | 12 | 1520-9210 |
Citations | PageRank | References |
2 | 0.39 | 23 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Joohyun Lee | 1 | 87 | 8.12 |
Kyunghan Lee | 2 | 1262 | 63.60 |
Choongwoo Han | 3 | 2 | 0.39 |
Tae-Hoon Kim | 4 | 459 | 53.02 |
Song Chong | 5 | 2113 | 143.72 |