Scalable Network and Transport Supports for QoE Fairness in Streaming Media Services

Video streaming applications have become ubiquitous and are predicted to account for 82% of global IP traffic by the year 2021[1]. Providers such as Netflix and Amazon increasingly seek to replace rather than complement the tradition television experience, aided by the availability of clients for multiple devices allowing multiple users to stream video content simultaneously.

There are competing industry standards aimed at ensuring quality of experience (QoE) for users, one example is Dynamic Adaptive Streaming over HTTP (DASH). Used by Netflix and YouTube among others, DASH is a codec-agnostic technique for delivering content from HTTP servers. Media is broken into equal length, non-overlapping segments, encoded at various bitrates and made available from the server. Client applications request media at the highest bitrate possible based on current network conditions. The requested bitrate can be varied from one segment to another depending on fluctuations in available bandwidth.

DASH and similar standards aim to ensure the best possible QoE for application users. However, given the shift in user habits, video streams now frequently compete with each other for bandwidth behind a common network bottleneck, most often a home router. Recent work in this area has shown the current adaptation mechanisms do not improve QoE, but instead lead to instability for users and unfairness between competing applications.

In order to provide a long term solution to this problem it is necessary to explore a new network- wide architecture that implements an approach similar to Transport-Independent Path Layer State Management[4] This builds upon the work in to enable client applications to expose the information necessary to optimise QoE at a network level.