Continuous Prediction of Streaming Video QoE Using Dynamic Networks
Streaming video data accounts for a large portion of mobile network traffic. Given the throughput and buffer limitations that currently affect mobile streaming, compression artifacts and rebuffering events commonly occur. Being able to predict the effects of these impairments on perceived video quality of experience (QoE) could lead to improved resource allocation strategies enabling the delivery of higher quality video. Toward this goal, we propose a first of a kind continuous QoE prediction engine. Prediction is based on a nonlinear autoregressive model with exogenous outputs. Our QoE prediction model is driven by three QoE-aware inputs: An objective measure of perceptual video quality, rebuffering-aware information, and a QoE memory descriptor that accounts for recency. We evaluate our method on a recent QoE dataset containing continuous time subjective scores.