LiveSR: enabling universal HD live video streaming with crowdsourced online learning

Zhenxiao Luo, Zelong Wang, Miao Hu, Yipeng Zhou, Di Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The high-definition (HD) live video streaming has gained significant popularity due to the rapid growth of 4G/5G 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.

Original languageEnglish
Pages (from-to)2788-2798
Number of pages11
JournalIEEE Transactions on Multimedia
Volume25
Early online date14 Feb 2022
DOIs
Publication statusPublished - 2023

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