LiveSR: Enabling Universal HD Live Video Streaming With Crowdsourced Online Learning

The high-definition (HD) live video streaming has gained significant popularity due to the rapid growth of 4 G/5 G and social media. However, for devices with constrained bandwidth, they still have no sufficient bandwidth to support HD live video streaming. In this paper, we propose a neural-enhanced HD live video streaming framework called LiveSR to provide universal HD live video streaming for both bandwidth-constrained and bandwidth-rich devices. For bandwidth-constrained devices, LiveSR delivers low-quality video streams and then boosts video quality at the device side with super-resolution (SR) techniques. The difficulty lies in how to train the SR model with low cost and conduct quality enhancement in real time. To address these challenges, we design a crowdsourced online training method by exploiting computation resources and HD video data on bandwidth-rich devices in the same video channel. We also propose an imitation learning-based decision making algorithm to make downloading decisions for video chunks and SR models under limited bandwidth. We implement and evaluate our proposed LiveSR framework using real network traces, and the experiment results show that LiveSR outperforms all the other baseline approaches, with 65.5% improvement in terms of the average QoE and 5.7% in terms of video quality (i.e., PSNR), and the achieved frame rate can be as high as 30 frames per second.

Automated summary

This paper proposes a neural-enhanced HD live video streaming framework called LiveSR, which aims to provide universal HD live video streaming for both bandwidth-constrained and bandwidth-rich devices. For bandwidth-constrained devices, LiveSR delivers low-quality video streams and enhances video quality at the device side using super-resolution (SR) techniques. To overcome the challenges of training the SR model with low cost and conducting real-time quality enhancement, a crowdsourced online training method and imitation learning-based decision-making algorithm are introduced. Experimental results show that LiveSR outperforms other baseline approaches, with 65.5% improvement in average QoE and 5.7% improvement in video quality (PSNR), achieving a frame rate as high as 30 frames per second.