TY - JOUR
T1 - Live MPEG-DASH video streaming cache management with cognitive mobile edge computing
AU - Weng, Hung Yen
AU - Hwang, Ren Hung
AU - Lai, Chin Feng
N1 - Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Video streaming is expected to account for up to 82% of network traffic by 2021 according to the forecast of CISCO's Visual Networking Index. Dynamic Adaptive Streaming over HTTP (DASH) is the de facto protocol for delivering video streaming services over the Internet. As the cellular network entering the 5G era, more and more video streaming will be live video streaming delivered to mobile phones. However, due to a large amount of video traffic, several techniques are required to improve the user Quality of Experience (QoE). In this work, we first propose a network architecture design for delivering live video streaming over the cellular core network with cognitive Mobile Edge Computing (MEC) servers. We then focused on the optimal cache management by considering several issues, include QoE, cache size, backhaul bandwidth, pre-cache mechanism, and user mobility. Finally, we show a prototype of the proposed MEC-assisted live video streaming system. Our simulation results show the performance improvement of the proposed cache management schemes in terms of QoE-based system utility. Our prototype shows the significant latency reduction in receiving video streams with MEC pre-cache mechanism.
AB - Video streaming is expected to account for up to 82% of network traffic by 2021 according to the forecast of CISCO's Visual Networking Index. Dynamic Adaptive Streaming over HTTP (DASH) is the de facto protocol for delivering video streaming services over the Internet. As the cellular network entering the 5G era, more and more video streaming will be live video streaming delivered to mobile phones. However, due to a large amount of video traffic, several techniques are required to improve the user Quality of Experience (QoE). In this work, we first propose a network architecture design for delivering live video streaming over the cellular core network with cognitive Mobile Edge Computing (MEC) servers. We then focused on the optimal cache management by considering several issues, include QoE, cache size, backhaul bandwidth, pre-cache mechanism, and user mobility. Finally, we show a prototype of the proposed MEC-assisted live video streaming system. Our simulation results show the performance improvement of the proposed cache management schemes in terms of QoE-based system utility. Our prototype shows the significant latency reduction in receiving video streams with MEC pre-cache mechanism.
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U2 - 10.1007/s12652-020-02549-z
DO - 10.1007/s12652-020-02549-z
M3 - Article
AN - SCOPUS:85091184653
SN - 1868-5137
JO - Journal of Ambient Intelligence and Humanized Computing
JF - Journal of Ambient Intelligence and Humanized Computing
ER -