S2VC: An SDN-based Framework for Maximizing QoE in SVC-Based HTTP Adaptive Streaming
Farzad Tashtarian, Alireza Erfanian, Amir Varasteh

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…
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