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

TL;DR

This paper introduces S2VC, an SDN-based framework that optimizes rate adaptation and data paths for SVC-based HTTP streaming, significantly enhancing QoE and fairness by leveraging centralized network control and optimization models.

Contribution

The paper presents a novel SDN-based framework with a centralized optimizer for maximizing QoE and fairness in SVC streaming, including a MILP formulation and LP relaxation for large-scale networks.

Findings

Improved QoE and fairness metrics demonstrated in emulation experiments.

Effective rate adaptation and path selection through centralized optimization.

Framework scalable to large networks using LP-relaxation.

Abstract

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.