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The cache and transcoding of the multi-access edge computing (MEC) server andwireless resource allocation in eNodeB interact and determine the quality ofexperience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients in MECnetworks. However, the relationship among the three factors has not beenexplored, which has led to limited improvement in clients' QoE. Therefore, wepropose a joint optimization framework of video segment cache and transcodingin MEC servers and resource allocation to improve the QoE of DASH clients.Based on the established framework, we develop a MEC cache management mechanismthat consists of the MEC cache partition, video segment deletion, and MEC cachespace transfer. Then, a joint optimization algorithm that combines videosegment cache and transcoding in the MEC server and resource allocation isproposed. In the algorithm, the clients' channel state and the playback statusand cooperation among MEC servers are employed to estimate the client'spriority, video segment presentation switch and continuous playback time.Considering the above four factors, we develop a utility function model ofclients' QoE. Then, we formulate a mixed-integer nonlinear programmingmathematical model to maximize the total utility of DASH clients, where thevideo segment cache and transcoding strategy and resource allocation strategyare jointly optimized. To solve this problem, we propose a low-complexityheuristic algorithm that decomposes the original problem into multiplesubproblems. The simulation results show that our proposed algorithmsefficiently improve client's throughput, received video quality and hit ratioof video segments while decreasing the playback rebuffering time, video segmentpresentation switch and system backhaul traffic.

Towards 5G: Joint Optimization of Video Segment Cache, Transcoding and  Resource Allocation for Adaptive Video Streaming in a Muti-access Edge  Computing Network