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.

翻译：随着移动设备在视频流媒体中的日益普及，优化这些设备上的流媒体体验变得至关重要。尽管基于深度学习的视频增强技术正受到广泛关注，但大多数技术无法在移动设备上支持实时增强。此外，许多技术仅专注于超分辨率，无法处理视频帧的局部或完全丢失或损坏问题——这在互联网和无线网络中十分常见。为了克服这些挑战，本文提出了一种新颖方法。该方法包括：(i) 一种创新的视频帧恢复方案，(ii) 一种新的超分辨率算法，以及(iii) 一种接收端增强感知的视频码率自适应算法。我们在iPhone 12上实现了该方法，可支持30帧/秒（FPS）。我们已在WiFi、3G、4G和5G等多种网络中对该方法进行了评估。评估结果表明，我们的方法能够实现实时增强，并在视频流媒体系统中使视频体验质量（QoE）显著提升24%至82%。