Overlay routing in Content Distribution Networks for Quality of Experience optimization

The constantly increasing amount of video traffic is a challenge for service providers that need to maximize Quality of Experience (QoE) while optimizing the cost of their infrastructure. In this thesis, we address the problem of routing multiple HTTP-based Adaptive Streaming (HAS) sessions to maximize QoE. We first design a model linking the Quality of Service (QoS) Metrics to a single QoE score. This QoS-QoE model incorporates different QoE metrics and is able to learn online network variations and predict their impact on representative classes of adaptation logic, video motion and client resolution. Different QoE metrics are then combined into a QoE score based on ITU-T Rec. P.1202.2. This rich score is used to formulate the routing problem. We show that, even with a piece-wise linear QoE function in the objective, the routing problem without controlled rate allocation is non-linear. We therefore express a routing-plus-rate allocation problem and make it scalable with a dual subgradient approach based on Lagrangian relaxation where subproblems select a single path for each request with a trivial search, thereby connecting explicitly QoE, QoE and HAS bitrate. We show with ns-3 simulations that our algorithm provides values for HAS QoE metrics (quality, rebufferings, variation) equivalent to MILP and better than QoS-based approaches. Since ns-3 simulator cannot take in account different traffic queues for the same link we have then designed an emulated SDN solution based on a virtual switch with a virtual remote network controller and multiple lightweight clients to analyze the network behavior in the single bottleneck case.