# Internet Video Delivery Improved by Super-Resolution with GAN
###### Abstract  
In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total. super-resolution; deep neural networks; GAN; streaming traffic; CDN; video delivery; cloud +
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With the adoption of adaptive bitrate technologies, published videos are encoded to fit a set of screen resolutions and playback bitrates as defined by the targeted audiences. This preprocessing stage happens in the cloud infrastructure, and the output, i.e., a bundle of video files, is moved around through contracted transport infrastructure. Content placement algorithms find the best fit between those files and targeted audiences, subject to strict cost-cutting goals. All of these efforts are constrained by the practices established in traditional video-publishing workflows, i.e., encoding recipes that set the target resolution and streaming bitrates as output by the end of the encoding phase. Based on these recipes, videos are downward encoded, bundled, and then moved and stored on selected surrogate servers. Moreover, despite all the recent advances in video-compression techniques, moving and storing encoded videos are naturally resource-demanding operations. For instance, a five-second video sequence can demand up to 97 MBytes to store and 156 Mbps to maintain its encoding bitrate during a streaming session that targets HD devices. A state-of-the-art video workflow implements per-title encoding using machine-learning techniques. These workflows find a set of distinct bitrates that have perceptual meaning to their audiences. In other words, switching from low-bitrate to high-bitrate streaming will improve the video session's perceived quality. Static-encoding recipes cannot deliver such assurances due to their generality. In [4], the per-title effectiveness was evaluated and showed an impressive 84% savings due to fewer bits per segment and fewer quality changes by the client. However, in such a video workflow, there is a set of encoded videos that require movement and storage to shorten the playtime. The optimization of Internet video delivery is the subject of many works [2; 5; 6; 7; 8], and these cover CDNs, HAS, CDN-P2P, CDN assisted by fog computing, and video SR on the client side, among others. However, video delivery still has many challenges to be solved [2; 9]. For example, the amount of video traffic on the Internet has been increasing yearly, heading towards an Internet bottleneck [1; 10]. Furthermore, international data traffic provided by Tier-1 ISPs is more expensive than regional and local traffic (Tier-2 and Tier-3 ISPs) [11].