In the context of next generation 5G networks, the satellite industry is clearly

Abstract-This paper presents a novel theoretical framework for end-to-end video quality prediction of MPEG-based video sequences. The proposed framework encloses two discrete models: i) A model for predicting the video quality of an encoded signal at a pre-encoding stage and ii) A model for mapping QoSsensitive network parameters (i.e. packet loss) to video quality degradation. The efficiency of both the discrete models is experimentally validated, proving by this way the accuracy of the proposed framework.

The focus of research into 5G networks to date has been largely on the required advances in network architectures, technologies, and infrastructures. Less effort has been put on the applications and services that will make use of and exploit the flexibility of 5G networks built upon the concept of software-defined networking (SDN) and network function virtualization (NFV). Media-based applications are amongst the most demanding services, requiring large bandwidths for high Manuscript

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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.

Abstract-This paper presents a novel theoretical framework for end-to-end video quality prediction of MPEG-based video sequences. The proposed framework encloses two discrete models: i) A model for predicting the video quality of an encoded signal at a pre-encoding stage and ii) A model for mapping QoSsensitive network parameters (i.e. packet loss) to video quality degradation. The efficiency of both the discrete models is experimentally validated, proving by this way the accuracy of the proposed framework.

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