JRA2: Joint Optimization of Resource Allocation and Rate Adaptation for DASH Services

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) has been broadly applied within most of mainstream video delivery services. At the same time, the lack of Quality of Experience (QoE) and the competition among several DASH streams have attracted significant attention. Aiming to supply better QoE for DASH streams in terms of video quality as well as starvation-free playing back, and achieve fairness among clients, we try to formulate the joint optimization of resource allocation and rate adaptation (JRA2) problem in this paper based on the information of streams' playout buffers and available network bandwidth. We first analyze the buffer behavior of a DASH stream using a modified version of M/D/1 queue and formulate the JRA2 problem based on the analysis. To solve this problem, we propose an algorithm based on Generalize Benders Decomposition (GBD) to get optimal solution (JRA2-G) and devise one heuristic algorithm (JRA2-A) for acceleration. Based on the sufficient demonstrating results, the proposed algorithms can supply high-quality and smooth playing back for clients.