  As mobile devices become increasingly popular for video streaming, it's
crucial to optimize the streaming experience for these devices. Although deep
learning-based video enhancement techniques are gaining attention, most of them
cannot support real-time enhancement on mobile devices. Additionally, many of
these techniques are focused solely on super-resolution and cannot handle
partial or complete loss or corruption of video frames, which is common on the
Internet and wireless networks.
  To overcome these challenges, we present a novel approach in this paper. Our
approach consists of (i) a novel video frame recovery scheme, (ii) a new
super-resolution algorithm, and (iii) a receiver enhancement-aware video bit
rate adaptation algorithm. We have implemented our approach on an iPhone 12,
and it can support 30 frames per second (FPS). We have evaluated our approach
in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows
that our approach enables real-time enhancement and results in a significant
increase in video QoE (Quality of Experience) of 24\% - 82\% in our video
streaming system.

Networking and Internet Architecture;Computer Vision and Pattern Recognition;Machine Learning;Multimedia