QoE-traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming

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Abstract

Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.

Details

Original languageEnglish
Pages (from-to)52261-52276
Number of pages15
JournalIEEE Access
Volume6
Publication statusPublished - Sep 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Collaborative caching, Dynamic adaptive video streaming over HTTP (DASH), Fairness, Integer non-linear programming, Multi-access edge computing (MEC), NP-hardness, Quality of experience, Dynamic adaptive video streaming over HTTP (DASH), Fairnes, Integer non-linear programming, Multi-access edge computing (MEC), NP-hardnes, Quality of experience

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