Optimized Preference-Aware Multi-path Video Streaming with Scalable Video Coding

TL;DR

This paper presents an application-layer multi-path video streaming algorithm using scalable video coding, optimizing quality and fairness without requiring OS kernel modifications, and demonstrates its effectiveness through extensive experiments.

Contribution

It introduces a novel application-layer multi-path streaming algorithm leveraging SVC, formulated as an optimization problem, with an efficient online solution that outperforms existing methods.

Findings

Significant QoE improvement over existing multi-path algorithms.

Robustness against bandwidth prediction errors.

Effective optimization of quality and fairness tradeoffs.

Abstract

Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users' quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The QoE is a weighted some of the following metrics: skip/stall duration, average playback rate, and quality switching rate. However, the weights are chosen such that pushing more chunks to the same quality level is more preferable over any other choice. Even though the formulation is a non-convex discrete optimization, we show that the problem can be solved optimally with a polynomial complexity in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.