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Energy optimization for mobile video streaming via an aggregate model

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Abstract

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user’s video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users’ personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing “user interest” and “video attractiveness”. We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.

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Notes

  1. https://support.google.com/youtube/answer/2853702?hl=en

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Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments. This work is supported in part by the National Natural Science Foundation of China (Grant nos. 61528206 and 61402380), the Natural Science Foundation of CQ CSTC (Grant no. cstc2015jcyjA40044), U.S. National Science Foundation (Grant nos. CNS-1253506 (CAREER) and CNS-1618300), the Fundamental Research Funds for the Central Universities (Grant no. XDJK2015B030), and the Opening Project of State Key Laboratory for Novel Software Technology (Grant No. KFKT2016B13).

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Authors and Affiliations

  1. College of Computer and Information Sciences, Southwest University, Chongqing, 400715, China

    Yantao Li

  2. Department of Computer Science, College of William and Mary, Williamsburg, VA, 23187, USA

    Yantao Li, Du Shen & Gang Zhou

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Jiangsu, 210023, China

    Yantao Li

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  1. Yantao Li
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  2. Du Shen
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Correspondence to Yantao Li.

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Li, Y., Shen, D. & Zhou, G. Energy optimization for mobile video streaming via an aggregate model. Multimed Tools Appl 76, 20781–20797 (2017). https://doi.org/10.1007/s11042-016-4002-1

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  • DOI: https://doi.org/10.1007/s11042-016-4002-1

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