  The cache and transcoding of the multi-access edge computing (MEC) server and
wireless resource allocation in eNodeB interact and determine the quality of
experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients in MEC
networks. However, the relationship among the three factors has not been
explored, which has led to limited improvement in clients' QoE. Therefore, we
propose a joint optimization framework of video segment cache and transcoding
in MEC servers and resource allocation to improve the QoE of DASH clients.
Based on the established framework, we develop a MEC cache management mechanism
that consists of the MEC cache partition, video segment deletion, and MEC cache
space transfer. Then, a joint optimization algorithm that combines video
segment cache and transcoding in the MEC server and resource allocation is
proposed. In the algorithm, the clients' channel state and the playback status
and cooperation among MEC servers are employed to estimate the client's
priority, video segment presentation switch and continuous playback time.
Considering the above four factors, we develop a utility function model of
clients' QoE. Then, we formulate a mixed-integer nonlinear programming
mathematical model to maximize the total utility of DASH clients, where the
video segment cache and transcoding strategy and resource allocation strategy
are jointly optimized. To solve this problem, we propose a low-complexity
heuristic algorithm that decomposes the original problem into multiple
subproblems. The simulation results show that our proposed algorithms
efficiently improve client's throughput, received video quality and hit ratio
of video segments while decreasing the playback rebuffering time, video segment
presentation switch and system backhaul traffic.
