DQ-DASH: AQueuing Theory Approach to Distributed Adaptive Video Streaming

Abstract

© 2020 ACM. The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a Distributed Queuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a Mx/D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.