Learning models for video quality prediction over wireless local area network and universal mobile telecommunication system networks

Universal mobile telecommunication system (UMTS) is a third-generation mobile communications system that supports wireless wideband multimedia applications. The primary aim of this study is to present learning models based on neural networks for objective, non-intrusive prediction of video quality over wireless local area network (WLAN) and UMTS networks for video applications. The contributions of this study are two-fold: first, an investigation of the impact of parameters both in the application and physical layer on end-to-end video quality is presented. The parameters considered in the application layer are content type (CT), sender bitrate (SBR) and frame rate (FR), whereas in the physical layer block error rate (BLER) and link bandwidth (LBW) are considered. Secondly, learning models based on adaptive neural fuzzy inference system (ANFIS) are developed to predict the visual quality in terms of the mean opinion score for all contents over access networks of UMTS and WLAN. ANFIS is well suited for video quality prediction over error-prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS-based artificial neural network is trained using a combination of physical layer parameters such as BLER and LBW and application layer parameters of CT, SBR and FR. The proposed models are validated using unseen data set. The preliminary results show that good prediction accuracy was obtained from the models. This study should help in the development of a reference-free video prediction model and quality of service control methods for video over UMTS/WLAN networks.