Energy optimized mobile video streaming with user behavior

Wenkui Xiang; Yiping Yuan; Yuming Su; Chao Yang; Dongming Tang

doi:10.1117/12.3031377

19 July 2024 Energy optimized mobile video streaming with user behavior

Wenkui Xiang, Yiping Yuan, Yuming Su, Chao Yang, Dongming Tang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131816A (2024) https://doi.org/10.1117/12.3031377
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the rapid development of mobile devices such as mobile phones, the internet traffic generated by mobile streaming media quickly surpassed all previous online entertainment applications. In order to reduce the energy consumption of mobile device users watching videos, we introduce an innovative Energy-Optimized Mobile Video Streaming Transmission System (EVSU) rooted in user behavior analysis. Leveraging a robust language model, EVSU scrutinizes video subtitle data, synthesizes content summaries, and predicts user viewing patterns. Employing an adaptive bitrate algorithm grounded in deep reinforcement learning, EVSU factors in users’ quality of experience (QoE), data efficiency, and energy consumption to dynamically select optimal video bitrates. Experimental results, using the popular documentary Planet Earth, showcase EVSU's remarkable ability to significantly reduce data transfer and energy consumption compared to existing algorithms, all while upholding a high user-perceived quality.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenkui Xiang, Yiping Yuan, Yuming Su, Chao Yang, and Dongming Tang "Energy optimized mobile video streaming with user behavior", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131816A (19 July 2024); https://doi.org/10.1117/12.3031377

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KEYWORDS

Video

Data modeling

Power consumption

Deep learning

Neural networks

Video processing

Machine learning

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