Abstract
The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.
Original language | English |
---|---|
Title of host publication | WWW '23: Proceedings of the ACM Web Conference 2023 |
Subtitle of host publication | Austin TX USA 30 April 2023- 4 May 2023 |
Editors | Ying Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben |
Place of Publication | New York |
Publisher | ACM |
Pages | 3121–3131 |
Number of pages | 11 |
ISBN (Electronic) | 9781450394161 |
DOIs | |
Publication status | Published - 30 Apr 2023 |
Keywords
- Mobile video streaming
- super-resolution
- video codec