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Accessing high-quality video content can be challenging due to insufficientand unstable network bandwidth. Recent advances in neural enhancement haveshown promising results in improving the quality of degraded videos throughdeep learning. Neural-Enhanced Streaming (NES) incorporates this new approachinto video streaming, allowing users to download low-quality video segments andthen enhance them to obtain high-quality content without violating the playbackof the video stream. We introduce BONES, an NES control algorithm that jointlymanages the network and computational resources to maximize the quality ofexperience (QoE) of the user. BONES formulates NES as a Lyapunov optimizationproblem and solves it in an online manner with near-optimal performance, makingit the first NES algorithm to provide a theoretical performance guarantee. Ourcomprehensive experimental results indicate that BONES increases QoE by 4% to13% over state-of-the-art algorithms, demonstrating its potential to enhancethe video streaming experience for users. Our code and data will be released tothe public.

BONES: Near-Optimal Neural-Enhanced Video Streaming