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. Our
comprehensive experimental results indicate that BONES increases QoE by 4% to
13% over state-of-the-art algorithms, demonstrating its potential to enhance
the video streaming experience for users. Our code and data will be released to
the public.

通过深度学习改善视频质量的神经增强对视频流媒体进行了探索，并引入了一种名为 BONES 的控制算法，以最大化用户体验的质量。实验证明相比最先进的算法，BONES 能够提高用户的体验质量（QoE）4％至 13％。