An ANFIS-based hybrid quality prediction model for H.264 video over UMTS networks

The Quality of Service (QoS) of Universal Mobile Telecommunication System (UMTS) is severely affected by the losses occurring in Radio Link Control (RLC) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour appropriately. In addition, video content has an impact on video quality under same network conditions. The aim of this paper is to present video quality prediction models for objective, non-intrusive prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over UMTS networks. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). 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 loss models considered are 2-state Markov models with variable Mean Burst Lengths (MBLs) depicting the various UMTS scenarios. The proposed model is trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from the model. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over UMTS networks.