SVC-based Multi-user Streamloading for Wireless Networks

TL;DR

This paper introduces a joint rate allocation and quality selection approach for streamloading, a novel video streaming scheme that improves user experience and network efficiency in wireless networks using scalable video coding.

Contribution

It proposes the first online algorithms for joint scheduling and quality selection in streamloading, outperforming existing schemes in network resource utilization and video quality adaptation.

Findings

Streamloading admits more streams at given video quality.

The proposed algorithms achieve higher quality adaptation performance.

Streamloading outperforms traditional streaming schemes in wireless networks.

Abstract

In this paper, we present an approach for joint rate allocation and quality selection for a novel video streaming scheme called streamloading. Streamloading is a recently developed method for delivering high quality video without violating copyright enforced restrictions on content access for video streaming. In regular streaming services, content providers restrict the amount of viewable video that users can download prior to playback. This approach can cause inferior user experience due to bandwidth variations, especially in mobile networks with varying capacity. In streamloading, the video is encoded using Scalable Video Coding, and users are allowed to pre-fetch enhancement layers and store them on the device, while base layers are streamed in a near real-time fashion ensuring that buffering constraints on viewable content are met. We begin by formulating the offline problem of jointly optimizing rate allocation and quality selection for streamloading in a wireless network. This motivates our proposed online algorithms for joint scheduling at the base station and segment quality selection at receivers. The results indicate that streamloading outperforms state-of-the-art streaming schemes in terms of the number of additional streams we can admit for a given video quality. Furthermore, the quality adaptation mechanism of our proposed algorithm achieves a higher performance than baseline algorithms with no (or limited) video-centric optimization of the base station's allocation of resources, e.g., proportional fairness.