Model-based QoE prediction to enable better user experience for video teleconferencing

The ultimate goal of network resource allocation for video teleconferencing is to optimize the Quality of Experience (QoE) of the video. We consider the IPPP video coding structure with macroblock intra refresh, which is widely used for video teleconferencing. Generally, the loss of a current frame causes error propagation to subsequent frames due to the video coding structure. Therefore, to optimize the QoE, a communication network needs to be able to accurately predict the consequence of each of its resource allocation decisions. We propose a QoE prediction scheme by considering QoE models that use the per-frame PSNR time series as the input, thus reducing the QoE prediction problem to a per-frame PSNR prediction problem. The QoE prediction scheme is jointly implemented by the video sender (orMCU) and the communication network. Simulation results show that the proposed per-frame PSNR prediction method is fairly accurate, with an average error well below 1dB.