TY - GEN
T1 - Bones
T2 - 2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2024
AU - Wang, Lingdong
AU - Singh, Simran
AU - Chakareski, Jacob
AU - Hajiesmaili, Mohammad
AU - Sitaraman, Ramesh K.
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/6/10
Y1 - 2024/6/10
N2 - 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.
AB - 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.
KW - adaptive bitrate streaming
KW - lyapunov optimization
KW - neural enhancement
KW - super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85196422524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196422524&partnerID=8YFLogxK
U2 - 10.1145/3652963.3655047
DO - 10.1145/3652963.3655047
M3 - Conference contribution
AN - SCOPUS:85196422524
T3 - SIGMETRICS/PERFORMANCE 2024 - Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
SP - 61
EP - 62
BT - SIGMETRICS/PERFORMANCE 2024 - Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
Y2 - 10 June 2024 through 14 June 2024
ER -