Jointly Video Bitrate Adaptation and Multicast Resource Allocation in Mobile Edge Networks

Abstract

Current schemes for Dynamic Adaptive Streaming over HTTP (DASH) are mainly client-driven. Thus, in the scenario of multiple users watching the same video, repeated subscription and data transmission results in an under-utilization of network bandwidth resources. Additionally, competition for limited network resources of individual users may motivate selfish behaviors, which leads to unfairness and sub-optimal utility of video services. In this paper, Multimedia Broadcast Multicast Service (MBMS) in mobile edge networks for multi-bitrate video sessions is applied to overcome these limitations. We formulate a non-linear integer programming (NLIP) model, which jointly optimize bitrate adaptation and resource allocation for multiple users. This model takes video quality, playback interruptions, and quality oscillations as linear constraints to maximize multicast users’ Quality of Experience (QoE). Due to NP-Hardness of this problem, we propose a heuristic greedy algorithm, which can work out the optimal or near-optimal solution with low time complexity. The evaluation results demonstrate that our method can achieve Pareto Optimality of the system utility, and maximize users’ QoE while ensuring fairness.