Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the bitrate to variations network conditions. This is especially important for current future Internet applications, including emerging ones such as virtual reality-based, adaptive plays a key role in providing high quality viewing experience, limited bandwidth delivery environments. To enable this promising avenue 5G context, efforts are made consider it alongside multicast edge caching, part next generation communication technology. In paper, we model transmission problem mobile scenario multi-source multi-rate (MMMP) whose linear relaxation concave. We decompose terms clients propose distributed algorithm (DDA). The computation complexity, convergence time-varying adaptation DDA theoretically analyzed. Additionally, further reduce complexity solution, heuristic approximation method (H-DDA) physical meaning proposed also shown how H-DDA converges optimal value numerical means. Finally, conduct series simulation tests demonstrate superiority HDDA comparison with other state-of-art solutions.