BONES: Near-Optimal Neural-Enhanced Video Streaming

Lingdong Wang, Simran Singh, Jacob Chakareski, Mohammad Hajiesmaili, Ramesh K. Sitaraman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in improving the quality of degraded videos through deep learning. Neural-Enhanced Streaming (NES) incorporates this new approach into video streaming, allowing users to download low-quality video segments and then enhance them to obtain high-quality content without violating the playback of the video stream. We introduce BONES, an NES control algorithm that jointly manages the network and computational resources to maximize the quality of experience (QoE) of the user. BONES formulates NES as a Lyapunov optimization problem and solves it in an online manner with near-optimal performance, making it the first NES algorithm to provide a theoretical performance guarantee. Comprehensive experimental results indicate that BONES increases QoE by 5% to 20% over state-of-the-art algorithms with minimal overhead. Our code is available at

Original languageEnglish (US)
Article number19
JournalProceedings of the ACM on Measurement and Analysis of Computing Systems
Issue number2
StatePublished - May 28 2024

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Computer Networks and Communications


  • adaptive bitrate streaming
  • lyapunov optimization
  • neural enhancement
  • super-resolution


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