Modeling streaming QoE in wireless networks with large-scale measurement of user behavior

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as buffering time and starvation behavior interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on realistic streaming traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the videos can be categorized into two classes, short and long viewing time with separated time scales. We then map the traffic pattern of large-scale measurement to bandwidth sharing cellular networks. We propose two models to compute the close-form starvation probability and mean sojourn time on the basis of ordinary differential equations (ODEs). Extensive trace-driven simulations validate their accuracy. The proposed models precisely capture how the QoE metrics of video streaming in each class are influenced by the scheduling algorithms at a base station.