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Title: BONES: Near-Optimal Neural-Enhanced Video Streaming
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.  more » « less
Award ID(s):
2136199 2106299 2102963 2045641 2106463 1901137 1763617 2106150 2032033
PAR ID:
10537279
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Volume:
8
Issue:
2
ISSN:
2476-1249
Page Range / eLocation ID:
1 to 28
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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