Bones: Near-optimal neural-enhanced video streaming

Document Type

Conference Proceeding

Publication Date

6-10-2024

Abstract

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 https://github.com/UMass-LIDS/bones.

Identifier

85196422524 (Scopus)

ISBN

[9798400706240]

Publication Title

SIGMETRICS/PERFORMANCE 2024 - Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

External Full Text Location

https://doi.org/10.1145/3652963.3655047

First Page

61

Last Page

62

Grant

CNS-2102963

Fund Ref

National Science Foundation

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