# Reducing System Load of Effective Video Using a Network Model
## 5 Conclusions  
The ideal in video streaming today is high-quality, persistent video. However, image quality may deteriorate depending on the user's system and the network environment, and a system with a higher specification may be required. In addition, it is thought that bandwidths currently used are insufficient to cover the streaming market in the future, as more broadcasts are showing real-time motion through VR or 360-degree video. In particular, videos with resolutions over 4K may have insufficient bandwidth depending on the streaming environment. In this paper, we have proposed a method to reduce system and network load according to the video viewing area. We aimed to reduce the bitrate by at least 30-40% and derived a bitrate reduction of about 40-60% using the proposed method. According to this method, after segmenting the image, SRGAN is applied to the FoV region to transmit an image of higher quality than the original image, and the DAIN + SST algorithm is applied to the non-FoV region to adjust FPS. In this way, we were able to solve the phenomenon of quality degradation that can be observed depending on the streaming environment. In addition, in order to reduce the burden on viewers due to the switching delay that occurs during region conversion using this method, streaming is enabled using the video region of the preprocessing part of the server. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **Bitrate** & **Data Rate** \\ \hline No. 1 (426\({}^{\circ}\)240) & 67 Kbps & 616 kb \\ No. 2 (426\({}^{\circ}\)240) & 78 Kbps & 710 kb \\ No. 3 (426\({}^{\circ}\)240) & 65 Kbps & 592 kb \\ No. 4 (426\({}^{\circ}\)240) & 92 Kbps & 833 kb \\ No. 5 (1704\({}^{\circ}\)960), FoV & 854 Kbps & 7622 kb \\ No. 6 (426\({}^{\circ}\)240) & 93 Kbps & 841 kb \\ No. 7 (426\({}^{\circ}\)240) & 83 Kbps & 752 kb \\ No. 8 (426\({}^{\circ}\)240) & 101 Kbps & 918 kb \\ No. 9 (426\({}^{\circ}\)240) & 85 Kbps & 776 kb \\ \hline Total & 1518 Kbps & 13,660 kb \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bitrate and size comparison for each game streaming video. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **Bitrate** & **Data Rate** \\ \hline No. 1(426\({}^{\circ}\)240) & 95 Kbps & 648 kb \\ No. 2(426\({}^{\circ}\)240) & 85 Kbps & 584 kb \\ No. 3(426\({}^{\circ}\)240) & 75 Kbps & 515 kb \\ No. 4(426\({}^{\circ}\)240) & 110 Kbps & 748 kb \\ No. 5(1704\({}^{\circ}\)960), FoV & 159 Kbps & 1069 kb \\ No. 6(426\({}^{\circ}\)240) & 97 Kbps & 661 kb \\ No. 7(426\({}^{\circ}\)240) & 80 Kbps & 549 kb \\ No. 8(426\({}^{\circ}\)240) & 98 Kbps & 667 kb \\ No. 9(426\({}^{\circ}\)240) & 90 Kbps & 613 kb \\ \hline Total & 891 Kbps & 6054 kb \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Bitrate and size comparison for each streaming video broadcast. In conclusion, using the above method, the user can transmit a high-quality image using a small capacity, and the viewer can watch the high-quality image. However, the use of various networks and algorithms requires a high-spec server. Therefore, in future research, instead of reducing the load on the network by reducing preprocessing as much as possible, we plan to conduct research to increase the FoV area, improve QoE, and reduce the load on the server. Conceptualization, S.-Y.C. ; methodology, S.-Y.C. ; software, S.-Y.C., D.-Y.K. and S.-Y.O. ; validation, S.-Y.C., D.-Y.K. and S.-Y.O. ; formal analysis, S.-Y.C., D.-Y.K. and S.-Y.O. ; investigation, S.-Y.C., D.-Y.K. and S.-Y.O. ; resources, S.-Y.C. ; data curation, S.-Y.C. ; writing--original draft preparation, S.-Y.C. ; writing--review and editing, S.-Y.C., D.-Y.K., S.-Y.O. and C.-B.S. ; visualization, S.-Y.C. ; supervision, C.-B.S. ; project administration, C.-B.S. All authors have read and agreed to the published version of the manuscript.