FAIR-AREA: A Fast AI-Based Joint Optimization of Rate Adaptation and Resource Allocation for DASH
Tongyu Song, Wenyu Hu, Wenshuai Xu, Jing Ren, Sheng Wang, Shizhong Xu · 2019
Video streaming service has been consuming a massive amount of Internet traffic during recent years. Even though Dynamic Adaptive Streaming over HTTP (DASH) has become the mainstream technology for improving users' Quality of Experience (QoE), the competing of multiple independent DASH streams could degrade the QoE and make unfair resource allocation. With the support of Software Defined Networking (SDN), it is possible to jointly optimizing resource allocation and bitrate adaptation in this competing scenario. In this paper, we propose FAIR-AREA, a fast Artificial Intelligence based joint optimization of rate adaptation and resource allocation of DASH service. With FAIR-AREA, we can solve this complex optimization problem only in milliseconds and achieve near optimal performance at the same time.