D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming

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Abstract

Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.

Details

Original languageEnglish
Title of host publicationIEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) 2019
Publication statusAccepted/In press - 12 Aug 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks -
Duration: 1 Jan 1900 → …

Conference

ConferenceIEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
Abbreviated titleWoWMoM
Period01/01/1900 → …

ID: 36040720