# Internet Video Delivery Improved by Super-Resolution with GAN
###### Abstract
Conversely, the computational power at the edge clouds has increased, which has likewise increased the volume of idle computing resources at the backend servers [12]. In addition, many video-processing tasks can be performed by GPUs, which have gained increased computational power in recent years. This trend in GPU performance has been reducing the processing costs of cloud services [13; 14]: e.g., in early 2020, Google Cloud reduced its GPU prices by more than 60% [15]. Using these resources to reconstruct and transcode videos at the edges will relieve the bottleneck that is restricting international Internet traffic in exchange for bringing content and processing closer to the consumers. Moreover, video-delivery and cloud-computing companies could establish a symbiotic business relationship. Machine learning (ML) has seen an unprecedented boom in applications that address problems and enable automation in a variety of disciplines [16; 17]. This is owing to an increase in data availability, significant advances in ML approaches, and breakthroughs in computational capabilities. Recent findings in neural deep learning have provided new venues for publishing videos on the Internet [18; 19; 20; 21; 22; 23; 24; 25]. These findings have shown that two neural deep-learning models, CNNs and GANs, can input low-resolution images and upscale them to high resolution [26]. This technique is called image SR, and according to such studies, its output and original images are similar in terms of perceptual quality. These models are the basis of our research, which yields the following contributions:  
* We propose a cloud-based content-placement framework that substantially reduces video traffic on long-distance infrastructures. In this framework, low-resolution videos move between servers in the cloud and the surrogate server deployed on the server side. An efficient SR GAN-based model reconstructs videos in high resolution. * We created a video SR model as a practical solution to use in a video-on-demand delivery system that upscales videos by a factor of 2 with perceptual quality indistinguishable from the ground truth. * We present a method for mapping the perceptual quality of reconstructed videos to the QP level representation of the same video. This method is essential for comparing the quality of a video reconstructed by SR with the representation of the same video at different compression levels. * Finally, we evaluate the contribution of SR to reducing the data and compare it with reduction by compression. Additionally, we analyze the advantages of the two approach combinations. Our experiments demonstrated that it is possible to reduce the amount of traffic in the cloud infrastructure by up to 98.42% when compared to video distribution with lossless compression. We organized this paper as follows. Section 2 discusses works on image and video super-resolution and perceptual video quality metrics. Section 3 presents the video framework proposed in this study. Section 4 offers the proposed SR model and its perceptual loss function. The datasets used to train and test models are presented in Section 5. Section 6 shows the video quality assessment metrics used in this work. The parameters, training details, and numerical results are shown in Section 7. Finally, in Section 8, our conclusions and future work are presented.