Paper Info
Title
Impact of RLC losses on quality prediction for H.264 video over UMTS networks
Abstract
In Universal Mobile Telecommunication System (UMTS) Radio Link Control (RLC) losses severely affect the Quality of Service (QoS) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour. In this paper we evaluate the impact of the radio access network on the end-to-end QoS for H.264 encoded video. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed that takes into account the RLC loss models to predict the video quality in terms of the Mean Opinion Score (MOS). The RLC loss models considered are 2-state Markov models with variable mean burst lengths. The aim of the paper is two-fold. First, to find the impact of QoS parameters in both the physical and application layer on end-to-end video quality. Second, to propose a prediction model based on ANFIS to predict video quality over UMTS networks. 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 model is trained with a combination of application and physical layer parameters. The performance of the proposed model is validated with unseen dataset. These studies should help in the understanding of the impact of both the application and physical layer parameters on end-to-end video quality and in QoS control methods and adaptation.
Year
DOI
Venue
2010
10.1109/ICME.2010.5583033
Multimedia and Expo
Keywords
Field
DocType
3G mobile communication,Markov processes,error statistics,fuzzy neural nets,quality of service,radio access networks,radio links,video coding,H.264 encoded video,Markov models,QoS,UMTS networks,adaptive neural fuzzy inference system,error probability,mean opinion score,neural networks,physical layer parameters,quality of service,radio access network,radio link control loss,universal mobile telecommunication system,video quality prediction,ANFIS,H.264,RLC,UMTS,video quality prediction
UMTS frequency bands,Computer science,Markov model,Quality of service,Mean opinion score,Real-time computing,Physical layer,Adaptive neuro fuzzy inference system,Video quality,Radio Link Control
Conference
ISSN
ISBN
Citations 
1945-7871
978-1-4244-7491-2
2
PageRank 
References 
Authors
0.38
0
5
Name
Order
Citations
PageRank
Asiya Khan123214.47
Lingfen Sun241838.26
Emmanuel C. Ifeachor325526.56
Jose Oscar Fajardo419524.80
Fidel Liberal534543.63