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Q-FDBA: improving QoE fairness for video streaming

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Abstract

Multiplayer video streaming scenario can be seen everywhere today as the video traffic is becoming the “killer” traffic over the Internet. The Quality of Experience fairness is critical for not only the users but also the content providers and ISP. Consequently, a QoE fairness adaptive method of multiplayer video streaming is of great importance. Previous studies focus on client-side solutions without network global view or network-assisted solution with extra reaction to client. In this paper, a pure network-based architecture using SDN is designed for monitoring network global performance information. With the flexible programming and network mastery capacity of SDN, we propose an online Q-learning-based dynamic bandwidth allocation algorithm Q-FDBA with the goal of QoE fairness. The results show the Q-FDBA could adaptively react to high frequency of bottleneck bandwidth switches and achieve better QoE fairness within a certain time dimension.

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Notes

  1. http://github.com/broadbent/scootplayer

  2. http://www.opendaylight.org

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Acknowledgements

This work is funded by: National Sci-Tech Support Plan of China under Grant No. 2014BAH02F03, and by Youth Science Foundation of Jilin Province of China under Grant No. 20160520011JH, and by Youth Sci-Tech innovation leader and team project of Jilin Province of China under GrantNo. 20170519017JH

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University (JLU), No. 2699 Qianjin Street, Changchun, China

    Jingyan Jiang, Liang Hu, Pingting Hao, Rui Sun, Jiejun Hu & Hongtu Li

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  1. Jingyan Jiang
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  2. Liang Hu
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  3. Pingting Hao
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  4. Rui Sun
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  5. Jiejun Hu
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  6. Hongtu Li
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Correspondence to Hongtu Li.

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Jiang, J., Hu, L., Hao, P. et al. Q-FDBA: improving QoE fairness for video streaming. Multimed Tools Appl 77, 10787–10806 (2018). https://doi.org/10.1007/s11042-017-4917-1

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