QoE optimization through in-network quality adaptation for HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, video content is split into segments and encoded into multiple qualities, such that the quality of a video can be dynamically adapted during the HTTP download process. This has given rise to intelligent video players that strive to maximize Quality of Experience (QoE) by adapting the displayed quality based on the user's available bandwidth and device characteristics. HAS-based techniques have been widely used in Over-the-Top (OTT) video services. Recently, academia and industry have started investigating the merits of HAS in managed IPTV scenarios. However, the adoption of HAS in a managed environment is complicated by the fact that the quality adaptation component is controlled solely by the end-user. This prevents the service provider from offering any type of QoE guarantees to its subscribers. Moreover, as every user independently makes decisions, this approach does not support coordinated management and global optimization. These shortcomings can be overcome by introducing additional intelligence into the provider's network, which allows overriding the client's decisions. In this paper we investigate how such intelligence can be introduced into a managed multimedia access network. More specifically, we present an in-network video rate adaptation algorithm that maximizes the provider's revenue and offered QoE. Furthermore, the synergy between our proposed solution and HAS-enabled video clients is evaluated.