Analysis and modelling Quality of Experience of video streaming under time-varying bandwidth

Abstract

We take an analytical approach to study the Quality of user Experience (QoE) for video streaming applications. Our propose is to characterise buffer starvations for streaming video in the presence of bandwidth that varies over time. This variation of the bandwidth is caused due to the frequent changes of channel states and mobility. Specifically we develop an analytical framework to investigate Quality of user Experience (QoE) for streaming by considering a Markov Modulated Fluid Model (MMFM) that accurately approximates the nature of network traffic. We drive the close-form expressions for calculating the distribution of starvation as well as start-up delay using partial differential equations (PDEs) and solve them using the Laplace Transform. We illustrate the results with the cases of the two-state Markov Modulated Fluid Model that is commonly used in multimedia applications. We compare our analytical model with simulation results using ns-3 under various operating parameters. Finally, we apply our analysis results to optimize the objective quality of experience (QoE) of media streaming realizing the tradeoff among different metrics incorporating user preferences on buffering ratio, startup delay and perceived quality.