Real-time QoE estimation of DASH-based mobile video applications through edge computing

Video applications using MPEG-DASH (Dynamic Adaptive Streaming over HTTP, such as YouTube and Netflix) have been dominating the Internet traffic in recent years. It is increasingly acknowledged that in order to provide video clients with better Quality-of-Experience (QoE), both content service providers and network operators need to be aware of clients' QoE in the first place. In this paper, we present a novel real-time QoE estimation system through edge computing, which has been implemented and deployed at a real LTE-A network edge. When equipped with such a system, any virtual network function (VNF) deployed in a mobile network will be able to infer all DASH clients' QoE under its coverage in real time, where no feedback from clients are needed. Furthermore, our scheme is able to work robustly in busy network environments involving air interface where packet errors frequently occur. The significance of such a scheme is the availability of accurate and real-time knowledge on user QoE through a very lightweight mechanism at the mobile edge, which can be instantaneously used for various content manipulation or resource adaptation operations in order to assure user QoE in dynamic conditions. Through experiments in a real LTE-A network, we demonstrate that our scheme is able to estimate DASH clients' QoE with very high accuracy with very low CPU and RAM footprint.