Large scale analysis of HTTP Adaptive Streaming in mobile networks

Abstract

HTTP Adaptive bitrate video Streaming (HAS) is now widely adopted by Content Delivery Network Providers (CDNPs) and Telecom Operators (Telcos) to improve user Quality of Experience (QoE). In HAS, several versions of videos are made available in the network so that the quality of the video can be chosen to better fit the bandwidth capacity of users. These delivery requirements raise new challenges with respect to content caching strategies, since several versions of the content may compete to be cached. In this paper we present analysis of a real HAS dataset collected in France and provided by a mobile telecom operator involving more than 485,000 users requesting adaptive video contents through more than 8 million video sessions over a 6 week measurement period. Firstly, we propose a fine-grained definition of content popularity by exploiting the segmented nature of video streams. We also provide analysis about the behavior of clients when requesting such HAS streams. We propose novel caching policies tailored for chunk-based streaming. Then we study the relationship between the requested video bitrates and radio constraints. Finally, we study the users' patterns when selecting different bitrates of the same video content. Our findings provide useful insights that can be leveraged by the main actors of video content distribution to improve their content caching strategy for adaptive streaming contents as well as to model users' behavior in this context.