research-article

Energy-Efficient Mobile Video Streaming: A Location-Aware Approach

Authors:

Fang LiuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 1

Article No.: 6, Pages 1 - 16

https://doi.org/10.1145/3102301

Published: 21 August 2017 Publication History

Abstract

Video streaming is one of the most widely used mobile applications today, and it also accounts for a large fraction of mobile battery usage. Much of the energy consumption is for wireless data transmission and is highly correlated to network bandwidth conditions. In periods of poor connectivity, up to 90% of mobile energy can be used for wireless data transfer. In this article, we study the problem of energy-efficient mobile video streaming. We make use of the observed correlation between bandwidth and user location, and also observe that a user’s location is predictable in many situations, such as when commuting to a known destination. Based on the user’s predicted locations and bandwidth conditions, we optimize wireless transmission times to achieve high quality video playback while minimizing energy use. We propose an optimal offline algorithm for this problem, which runs in O(Tk) time, where T is the duration of the video and k is the size of the video buffer. We also propose LAWS, a Location AWare Streaming algorithm. LAWS learns from historical location-aware bandwidth conditions and predicts future bandwidths along a planned route to make online wireless download decisions. We evaluate LAWS using real bandwidth traces, and show that LAWS closely approximates the performance of the optimal offline algorithm, achieving 90.6% of the optimal performance on average, and 97% in certain cases. LAWS also outperforms three popular strategies used in practice by, on average, 69%, 63%, and 38%, respectively. Lastly, we show that LAWS is able to deal with noisy data and can attain the stated performance after sampling bandwidth conditions only five times.

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Index Terms

Energy-Efficient Mobile Video Streaming: A Location-Aware Approach
1. Networks
  1. Network types
    1. Mobile networks

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 1

Regular Papers and Special Issue: Data-driven Intelligence for Wireless Networking

January 2018

258 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3134224

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2017

Accepted: 01 December 2016

Revised: 01 November 2016

Received: 01 August 2016

Published in TIST Volume 9, Issue 1

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Singapore BCA Green Buildings Innovation Cluster (GBIC) R8D
Singapore NRF - Energy Innovation Research Program (EIRP)
Singapore MOE Tier-1

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Ma XHe WGao Y(2023)Virtual Machine Migration Strategy Based on Markov Decision and Greedy Algorithm in Edge Computing EnvironmentWireless Communications & Mobile Computing10.1155/2023/64417912023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6441791
Liao HTang GGuo DWu KWu Y(2022)EdgeSaver: Edge-Assisted Energy-Aware Mobile Video Streaming for User Retention EnhancementIEEE Internet of Things Journal10.1109/JIOT.2021.31116459:9(6550-6562)Online publication date: 1-May-2022
https://doi.org/10.1109/JIOT.2021.3111645
Machidon OAsprov JFajfar TPejović V(2022)Context-aware adaptation of mobile video decoding resolutionMultimedia Tools and Applications10.1007/s11042-022-13787-y82:12(17599-17630)Online publication date: 3-Oct-2022
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Xu GFeng GJiao LFeng MZheng XLiu J(2021)FNetSecurity and Communication Networks10.1155/2021/53957052021Online publication date: 1-Jan-2021
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