# Reducing System Load of Effective Video Using a Network Model
## 1 Introduction  
Recently, various fields and services, including cloud computing, have been growing at a greater rate than expected, and related systems are being developed. The live streaming industry, especially, is growing faster than ever. High-performance portable devices are popular for watching video content anytime, anywhere through a network, such as a Wi-Fi or 5g network [1]. However, when using real-time streaming, the simultaneous use of platforms, networks, and streaming formats can cause various problems for users and viewers. For example, connection delays due to network congestion or limits to server capacity due to excessive user access may result in great inconvenience. This paper proposes a new method, focusing on reducing the amounts of data transmitted and improving video quality as a solution to various problems with real-time streaming services [2].  
In this paper, we propose the following method, considering that the solution to various problems in real-time streaming services is to maintain and improve video quality and reduce the amount of data transmitted. First, the problem is solved by using the characteristics of the non-FoV region existing in the image apart from the viewing region. During streaming or learning through a network, the video transmitted to the user shows all areas of the image; therefore, more time is taken up and unnecessary data consumption occurs in transmission, which causes problems such as high traffic. To avoid this, we used screen division to maintain image quality without compromising QoE (Quality of Experience). Through this, we tried to reduce the overall traffic of the network by fixing the quality of FoV and lowering the quality of the non-FoV area. Second, system load was reduced by varying the learning method according to the viewing area. The reason for dividing into an FoV region and a non-FoV region is to apply deep learning to each image. Previously, a method was used to reduce system load by only guaranteeing the quality of the FoV region through segmentation and treating the non-FoV region as background. However, this only reduced the load on the system and could not maintain the quality for the entire area. When the change in viewpoint was applied, the viewer had no choice but to check that the quality of the image changed slowly. We therefore applied thistechnique to maintain the minimum frame and quality even outside the viewing area. The viewing area transmits high-quality images and the non-FoV area uses low-quality images to which DAIN (Depth-Aware Video Frame In-interpolation) is applied, thereby reducing network load and transmission. Third, the user does not continuously transmit the area that the viewer first saw, but selectively transmits the image as FoV information is received. This allows for real-time transmission while maintaining image quality, since the viewing area can change continuously. Finally, SRGAN (Super Resolution Generative Advisory Networks) is applied to the FoV area to maintain and improve image quality, and DAIN and SST are applied to the non-FoV area to adjust the image. The image that is the result this process is designed to be a selective output determined by the viewpoint of the viewer.