QoE-driven rate adaptation algorithm for fair dynamic adaptive video streaming in named data networking

Abstract

Nowadays, video streaming causes traffic over the Internet. Dynamic Adaptive video Streaming (DAS) has emerged as a promising solution. However, existing researches in DAS focus on single client instead of multi-client which is much closer to reality. Even if some methods are proposed for multi-client scenarios, they need to add coordinate proxy to the network or add additional function to routers or servers. Besides, the caches in Named Data Networking (NDN) have a significant impact on clients. Consequently, we proposed a QoE-driven fair-DAS algorithm, able to learn and dynamically adapt its behavior depending on network conditions. By evaluating this novel approach through simulations in several multi-client scenarios and comparing with the Rate and Buffer Based Adaptation (RBBA) algorithm, we are able to show that our algorithm resulted in a better video quality, fairness, stability and effectiveness, so a great improvement in overall QoE.