  HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery
technique for adaptive streaming over the Internet. Still considered as its
primary challenges are determining the optimal rate adaptation and improving
both the quality of experience (QoE) and QoE-fairness. Most of the proposed
approaches have relied on local information to find a result. However,
employing techniques that provide a comprehensive and central view of the
network resources can lead to more gains in performance. By leveraging software
defined networking (SDN), this paper proposes an SDN-based framework, named
S2VC, to maximize QoE metrics and QoE-fairness in SVC-based HTTP adaptive
streaming. The proposed framework determines both the optimal adaptation and
data paths for delivering the requested video files from HTTP-media servers to
DASH clients. In fact, by utilizing an SDN controller and its complete view of
the network, we introduce an SVC flow optimizer (SFO) application module to
determine the optimal solution in a centralized and time slot fashion. In the
current approach, we first formulate the problem as a mixed integer linear
programming (MILP) optimization model. The MILP is designed in such a way that
it applies defined policies, e.g. setting priorities for clients in obtaining
video quality. Secondly, we show that this problem is NP-complete and propose
an LP-relaxation model to enable S2VC framework for performing rate adaptation
on a large-scale network. Finally, we conduct experiments by emulating the
proposed framework in Mininet, with the usage of Floodlight as the SDN
controller. In terms of improving QoE-fairness and QoE metrics, the
effectiveness of the proposed framework is validated by a comparison with
different approaches.
