Exploration and Evaluation of Self-Clocked Rate Adaptation for Multimedia (SCReAM) Congestion Control Algorithm in 5G Networks

The new emerging mobile technologies have led to increased complexity in the networking paradigm. These technologies are incorporated into each device to support to-day's trends. Although this process offers many advantages, it brought several issues when new use-cases and services are introduced such as requiring regular updating of infrastructure, high network throughput, low latency, and minimum packet loss to provide proper performance. One of the main interesting applications that can benefit from such features is the conver-sational video for WebRTC which is becoming more popular since the developing community is growing and the industry is expressing its interest by striving towards designing new schemes for it. The main concept that controls the conversational video for WebRTC is the congestion control algorithm. Many approaches have been designed to address certain issues to result in optimum performance. In this study, we aim to explore the possibility of achieving optimal streaming in a 5G environment. We are especially interested in choosing the appropriate congestion control algorithm to achieve this purpose. As a candidate, we are focusing on the Self-Clocked Rate Adaptation for Multimedia (SCReAM) congestion control algorithm. Also, we aim to provide guidelines for achieving the video streaming performance of SCReAM in 5G networks.