Cache-Enabled Adaptive Video Streaming: A QoE-Based Evaluation Study

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) has prevailed as the dominant way of video transmission over the Internet. This technology is based on receiving small sequential video segments from a server. However, one challenge that has not been adequately examined is the obtainment of video segments in a way that serves both the needs of the network and the improvement in the Quality of Experience (QoE) of the users. One effective way to achieve this is to implement and study caching and DASH technologies together. This paper investigates this issue by simulating a network with multiple video servers and a video client. It then implements both the peer-to-many communications in the context of adaptive video streaming and the video server caching algorithm based on proposed criteria that improve the status of the network and/or the user. Specifically, we investigate the scenario of delivering DASH-based content with the help of an intermediate server, apart from a main server, to demonstrate possible caching benefits for different sizes of intermediate storage servers. Extensive experimentation using emulation reveals the interplay and delicate balance between caching and DASH, guiding such network design decisions. A general tendency found is that, as the available buffer size increases, the video playback quality increases to some extent. However, at the same time, this improvement is linked to the random cache selection algorithm.