QoE-SDN APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming

While video streaming has dominated the Internet traffic, video service providers (VSPs) compete on how to assure the best quality of experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the mobile network operators' (MNOs) infrastructure and evolving network conditions. Software-defined networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN App, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the Self-similar Least-Action Walk model is introduced, toward proactive segment caching. A number of use cases, enabled by the QoE-SDN App, are designed to evaluate the proposed scheme, revealing QoE benefits for VSPs and bandwidth savings for MNOs.