  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.
