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HTTP adaptive streaming (HAS) is quickly becoming the dominant video deliverytechnique for adaptive streaming over the Internet. Still considered as itsprimary challenges are determining the optimal rate adaptation and improvingboth the quality of experience (QoE) and QoE-fairness. Most of the proposedapproaches have relied on local information to find a result. However,employing techniques that provide a comprehensive and central view of thenetwork resources can lead to more gains in performance. By leveraging softwaredefined networking (SDN), this paper proposes an SDN-based framework, namedS2VC, to maximize QoE metrics and QoE-fairness in SVC-based HTTP adaptivestreaming. The proposed framework determines both the optimal adaptation anddata paths for delivering the requested video files from HTTP-media servers toDASH clients. In fact, by utilizing an SDN controller and its complete view ofthe network, we introduce an SVC flow optimizer (SFO) application module todetermine the optimal solution in a centralized and time slot fashion. In thecurrent approach, we first formulate the problem as a mixed integer linearprogramming (MILP) optimization model. The MILP is designed in such a way thatit applies defined policies, e.g. setting priorities for clients in obtainingvideo quality. Secondly, we show that this problem is NP-complete and proposean LP-relaxation model to enable S2VC framework for performing rate adaptationon a large-scale network. Finally, we conduct experiments by emulating theproposed framework in Mininet, with the usage of Floodlight as the SDNcontroller. In terms of improving QoE-fairness and QoE metrics, theeffectiveness of the proposed framework is validated by a comparison withdifferent approaches.

S2VC: An SDN-based Framework for Maximizing QoE in SVC-Based HTTP Adaptive Streaming