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There are many parameters that affect video quality but their combined effect is not well identified and understood when video is transmitted over mobile/ wireless networks. In addition, video content has an impact on video quality under same network conditions. The main aim of this paper is the prediction of video quality combining the application and network level parameters for all content types. Firstly, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, to study and analyze the behaviour of video quality for wide range variations of a set of selected parameters. Finally, to develop two learning models based on - (1) ANFIS to estimate the visual perceptual quality in terms of the Mean Opinion Score (MOS) and decodable frame rate (Q value) and (2) regression modeling to estimate the visual perceptual quality in terms of the MOS. We trained three ANFIS-based ANNs and regression based- models for the three distinct content types using a combination of network and application level parameters and tested the two models using unseen dataset. We confirmed that the video quality is more sensitive to network level compared to application level parameters. Preliminary results show that a good prediction accuracy was obtained from both models. However, the regression based model performed better in terms of the correlation coefficient and the root mean squared error. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over wireless/mobile networks. [ABSTRACT FROM AUTHOR]

The relative velocity between mobile devices is one of the key factors that determine the quality of communications in Mobile Ad-hoc NETworks (MANETs). Velocity estimates are useful in various aspects of ad hoc networking (e.g., predicting link lifetime and measuring performance). Conventional ways of estimating velocity rely on the availability of positioning systems such as the Global Positioning System (GPS) or precise knowledge of the characteristics of wireless channels/signals. A recently proposed method exploits time-varying internode range information for velocity estimations, provided that the range estimates are noise free. In this paper, we propose a new range-based method for relative velocity estimations (RVEs). We derive two range-based relative velocity estimators for both sparse and dense ad hoc networks, i.e., RVEs and RVEd. In addition to being less dependent on the characteristics of the wireless channel, the proposed method is more tolerant of the multipath or nonline-of-sight (NLOS) errors contained in range measurements than the existing method. Simulation results show an excellent match between the velocity estimates given by the proposed method and the actual values in both sparse and dense network cases, regardless of the nodal speed limits or the distribution of noises. In comparison with the existing method, the proposed method is shown to achieve an improvement factor of about 33 (in terms of normalized bias) with Gaussian multipath noises of 4-m standard deviation or 20 with uniform NLOS noises of 32-m maximum standard deviation. [ABSTRACT FROM AUTHOR]

The primary aim of this paper is to present new models for objective, nonintrusive, prediction of voice quality for IP networks and to illustrate their application to voice quality monitoring and playout buffer control in VoIP networks. The contributions of the paper are threefold. First, we present a new methodology for developing perceptually accurate models for nonintrusive prediction of voice quality which avoids time-consuming subjective tests. The methodology is generic and as such it has wide applicability in multimedia applications. Second, based on the new methodology, we present efficient regression models for predicting conversational voice quality nonintrusively for four modern codecs (G.729, G.723.1, AMR and iLBC). Third, we illustrate the usefulness of the models in two main applications - voice quality prediction for real Internet VoIP traces and perceived quality-driven playout buffer optimization. For voice quality prediction, the results show that the models have accuracy close to the combined ITU PESQ/E-model method using real Internet traces (correlation coefficient over 0.98). For playout buffer optimization, the proposed buffer algorithm provides an optimum voice quality when compared to five other buffer algorithms for all the traces considered [ABSTRACT FROM PUBLISHER]