Abstract
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 Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are 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 both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.
Highlights
Universal Mobile Telecommunication System (UMTS) is a third generation (3G), wireless cellular network based on Wideband Code Division Multiple Access technology, designed for multimedia communication
The accuracy of the proposed video quality prediction models is determined by the correlation coefficient and the Root Mean Squared Error (RMSE) of the validation results
The model is trained with three distinct content types from parameters both in the application and physical layers over UMTS networks
Summary
Universal Mobile Telecommunication System (UMTS) is a third generation (3G), wireless cellular network based on Wideband Code Division Multiple Access technology, designed for multimedia communication. Significant business potential has been opened up by the convergence of communications, media, and broadcast industries towards common technologies by offering entertainment media and broadcast content to mobile user For such services to be successful, the users Quality of Service (QoS) is likely to be the major determining factor. The aim is to develop learning models to predict video quality for all content types from both application and physical layer parameters for video streaming over UMTS networks as shown in. The quality of a video sequence is dependent on the spatiotemporal dynamics of the content It is known from the fundamental principles of the video coding theory that action clips with high dynamic content are perceived as degraded in comparison to the sequences with slow-moving clips, subject to identical encoding procedures
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