BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming

Qian Yu, Qing Li, Rui He, Gareth Tyson, Wanxin Shi, Jianhui Lv, Zhenhui Yuan, Peng Zhang, Yulong Lan, Zhicheng Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationWWW '23: Proceedings of the ACM Web Conference 2023
Subtitle of host publicationAustin TX USA 30 April 2023- 4 May 2023
EditorsYing Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben
Place of PublicationNew York
Number of pages11
ISBN (Electronic)9781450394161
Publication statusPublished - 30 Apr 2023

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