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.

翻译：高质量视频内容的获取常因网络带宽不足且不稳定而面临挑战。近年来，神经增强技术在通过深度学习改善退化视频质量方面展现出显著成效。神经增强流（NES）将这一新方法融入视频流中，允许用户下载低质量视频段，随后对其进行增强以获得高质量内容，而不影响视频流的播放。我们提出了BONES，一种NES控制算法，该算法联合管理网络与计算资源，以最大化用户的体验质量（QoE）。BONES将NES形式化为一个李雅普诺夫优化问题，并以在线方式求解，实现近优性能，成为首个具备理论性能保证的NES算法。我们的综合实验结果表明，与最先进算法相比，BONES将QoE提高了4%至13%，展现了其在提升用户视频流体验方面的潜力。我们的代码与数据将向公众开放。