An Augmented Autoregressive Approach to HTTP Video Stream Quality Prediction
This work addresses video streaming quality prediction for users, but it is incremental as it builds on existing methods by adding multiple inputs and forecasting ensembles.
The paper tackled the problem of predicting continuous-time subjective quality for HTTP video streaming to optimize bitrate allocation, resulting in considerably improved prediction performance and decreased forecasting errors.
HTTP-based video streaming technologies allow for flexible rate selection strategies that account for time-varying network conditions. Such rate changes may adversely affect the user's Quality of Experience; hence online prediction of the time varying subjective quality can lead to perceptually optimised bitrate allocation policies. Recent studies have proposed to use dynamic network approaches for continuous-time prediction; yet they do not consider multiple video quality models as inputs nor consider forecasting ensembles. Here we address the problem of predicting continuous-time subjective quality using multiple inputs fed to a non-linear autoregressive network. By considering multiple network configurations and by applying simple averaging forecasting techniques, we are able to considerably improve prediction performance and decrease forecasting errors.