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2016 IEEE 41st Conference on Local Computer Networks (LCN) (LCN) (2016)
Dubai, United Arab Emirates
Nov. 7, 2016 to Nov. 10, 2016
ISBN: 978-1-5090-2054-6
pp: 147-155
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.
Streaming media, Vehicles, Cellular networks, Correlation, IEEE 802.11 Standard, Bandwidth

F. Jiang, Z. Liu, K. Thilakarathna, Z. Li, Y. Ji and A. Seneviratne, "TransFetch: A Viewing Behavior Driven Video Distribution Framework in Public Transport," 2016 IEEE 41st Conference on Local Computer Networks (LCN), Dubai, United Arab Emirates, 2017, pp. 147-155.
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