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Dependency- and similarity-aware caching for HTTP adaptive streaming

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Abstract

There has been significant interest in the use of HTTP adaptive streaming for live or on-demand video over the Internet in recent years. To mitigate the streaming transmission delay and reduce the networking overhead, an effective and critical approach is to utilize cache services between the origin servers and the heterogeneous clients. As the underlying protocol for web transactions, HTTP has great potentials to explore the resources within state-of-the-art CDNs for caching; yet distinct challenges arise in the HTTP adaptive streaming context. After examining a long-term and large-scale adaptive streaming dataset as well as statistical analysis, we demonstrate that the switching requests among the different qualities frequently emerge and constitute a significant portion in a per-day view. Consequently, they have substantially affected the performance of cache servers and Quality-of-Experience (QoE) of viewers. In this paper, we propose a novel cache model that captures the dependency among the segments in the cache server for adaptive HTTP streaming. Our work does not assume any specific selection algorithm on the client’s side and hence can be easily incorporated into existing streaming cache systems. Its centralized nature is also well accommodated by the latest DASH specification. Moreover, we extend our work to the multi-server caching context and present a similarity-aware allocation mechanism to enhance the caching efficiency. The performance evaluation shows our dependency- and similarity-aware strategy can significantly improve the cache hit-ratio and QoE of HTTP streaming as compared to previous approaches.

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Notes

  1. For simplicity, we only show the video layers and segments. The general streaming components consist of video layers, audio tracks, subtitles of different languages, multiple DRM (Digital Rights Management) information and common encryption.

  2. http://www.neubot.org/

  3. A popular, free and open source cross-platform multimedia player and framework. http://www.videolan.org/vlc/index.html

  4. In this paper, we omit the subscript and superscript when there is no confusion.

  5. https://vine.co

  6. http://aws.amazon.com/ec2/

  7. Without loss of generality, we choose the first ten segments from 20-quality versions in the dataset of Big Buck Bunny: http://www-itec.uni-klu.ac.at/ftp/datasets/DASHDataset2014/BigBuckBunny/4sec/

  8. The instances are launched on Intel Xeon E5-2676 v3 2.4GHz Processor. Configuration: 2 vCPU; 8GB memory; 8G storage size; Enhanced Networking setting.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Cong Zhang & Jiangchuan Liu

  2. School of Digital Media, Jiangnan University, Wuxi, People’s Republic of China

    Fei Chen

  3. Department of Computer Science, Tsinghua University, Beijing, People’s Republic of China

    Yong Cui

  4. Department of Information Technology, Uppsala University, Uppsala, Sweden

    Edith C.-H. Ngai

  5. College of Natural Resources and Environment, South China Agricultural University, Guangzhou, People’s Republic of China

    Jiangchuan Liu & Yuemin Hu

Authors
  1. Cong Zhang
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  2. Jiangchuan Liu
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  3. Fei Chen
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  4. Yong Cui
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  5. Edith C.-H. Ngai
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Correspondence to Jiangchuan Liu.

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Zhang, C., Liu, J., Chen, F. et al. Dependency- and similarity-aware caching for HTTP adaptive streaming. Multimed Tools Appl 77, 1453–1474 (2018). https://doi.org/10.1007/s11042-016-4308-z

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  • DOI: https://doi.org/10.1007/s11042-016-4308-z

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