TransFetch: A Viewing Behavior Driven Video Distribution Framework in Public Transport

Fangzhou Jiang, Zhi Liu, Kanchana Thilakarathna, Zhenyu Li, Yusheng Ji, Aruna Seneviratne

研究成果: Conference contribution

4 引用 (Scopus)

抜粋

Mobile video traffic is exploding and it is particularly challenging to stream video when high density of users are "on the move", e.g., in public transport systems. It becomes increasingly problematic as video traffic is predicted to account for more than 80% of Internet traffic by 2019. This will be exacerbated by factors such as cellular network coverage issues and unstable network throughput due to high speed mobility. By exploiting the predictable public transport mobility patterns, spatio-temporal correlation of user interests and users' video viewing behaviors, we proposed TransFetch which uses intelligent caching on-board the public transport vehicles as well as a novel video chunk placement algorithm. We show through extensive simulations, that TransFetch reduces the system cellular data usage by up to 45% and improves the quality of video streaming by up to 35%. Finally, we demonstrate the practical feasibility of TransFetch by implementing caching units on a Raspberry-Pi and a mobile app on an Android device.

元の言語English
ホスト出版物のタイトルProceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016
出版者IEEE Computer Society
ページ147-155
ページ数9
ISBN(電子版)9781509020546
DOI
出版物ステータスPublished - 2016 12 22
イベント41st IEEE Conference on Local Computer Networks, LCN 2016 - Dubai, United Arab Emirates
継続期間: 2016 11 72016 11 10

Other

Other41st IEEE Conference on Local Computer Networks, LCN 2016
United Arab Emirates
Dubai
期間16/11/716/11/10

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

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  • これを引用

    Jiang, F., Liu, Z., Thilakarathna, K., Li, Z., Ji, Y., & Seneviratne, A. (2016). TransFetch: A Viewing Behavior Driven Video Distribution Framework in Public Transport. : Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016 (pp. 147-155). [7796773] IEEE Computer Society. https://doi.org/10.1109/LCN.2016.27