Bayesian Hierarchical Regression Models for QoE Estimation and Prediction in Audiovisual Communications

Abstract

This paper presents a trial of establishing a framework for Bayesian statistical modeling of quality of experience (QoE) estimation and prediction in multimedia IP communications. As an example, we take a bandwidth guaranteed interactive audiovisual communication system with a QoE enhancement mechanism. By Bayesian statistical analysis, we demonstrate how QoE is affected by factors of the channel bandwidth, contents of tasks, customization of playout buffering control for QoE enhancement, users' individualities, and gender. Adopting a latent variable approach, we build Bayesian regression models with covariates of the factors and random effect terms having hierarchical priors for the users' attributes. We estimate the posterior probabilities of parameters in the models by performing Markov chain Monte Carlo simulations with WinBUGS. From among the models, we select the most plausible one and explore the sensitivity to priors. We then evaluate the QoE measure, which is the posterior mean of overall satisfaction measured as five-point scores, and examine the effects of the factors on the QoE through odds ratios. Also, the Bayesian model makes predictions of score frequencies and compares them with measured ones to reveal high accuracy of the prediction. In addition, a cross-validatory approach to the model checking is taken for investigating the adequacy of the model; the posterior predictive p-values of a discrepancy function of the model indicate that the model is satisfactory.