An important and unsolved problem today is that of automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this task in real time (typically for streams sent themselves in real time) is especially interesting. The problem is still unsolved because there are many parameters affecting video quality, and their combined effect is not well identified and understood. Among these parameters, we have the source bit rate, the encoded frame type, the frame rate at the source, the packet loss rate in the network, etc. Only subjective evaluations give good results but, by definition, they are not automatic. We have previously explored the possibility of using artificial neural networks (NNs) to automatically quantify the quality of video flows and we showed that they can give results well correlated with human perception. In this paper, our goal is twofold. First, we report on a significant enhancement of our method by means of a new neural approach, the random NN model, and its learning algorithm, both of which offer better performances for our application. Second, we follow our approach to study and analyze the behavior of video quality for wide range variations of a set of selected parameters. This may help in developing control mechanisms in order to deliver the best possible video quality given the current network situation, and in better understanding of QoS aspects in multimedia engineering.Index Terms-Packet video, random neural networks, real-time video transmission, video quality assessment, video signal characterization.

Video services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessment methods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a noreference QoE monitoring module for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE.

We propose a novel simulation-based method that exploits a generalized splitting (GS) algorithm to estimate the reliability of a graph (or network), defined here as the probability that a given set of nodes are connected, when each link of the graph is failed with a given (small) probability. For large graphs, in general, computing the exact reliability is an intractable problem and estimating it by standard Monte Carlo methods poses serious difficulties, because the unreliability (one minus the reliability) is often a rare-event probability. We show that the proposed GS algorithm can accurately estimate extremely small unreliabilities and we exhibit large examples where it performs much better than existing approaches. It is also flexible enough to dispense with the frequently made assumption of independent edge failures.

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Abstiact-Interval availability is a dependability measure defined by the fraction of thne during which a system is operational over a finite observation period. The computation of its distribution allows the user to ensure that the probability that its system will achieve a given availability level is high enough.The system is assumed to be modeled as a Markov process with countable state space. We propose a new algorithm to compute the interval availability distribution. One of its main advantages is that, in some cases, it applies even to infinite state spaces. This is useful, for instance, in case of models taking into account contention with unbounded buffers. This important feature is illustrated on models of multiprocessor systems subject to breakdowns and repair. When the model is finite, we show through a numerical example that the new technique can perform very well.Index Terms-Denumerable Markov processes, dependability prediction, interval availability distribution, repairable computer systems, transient analysis, queues with breakdowns, uniformization. I. INT~~~DuCTI~NI N the dependability analysis of repairable computing systems, there is an increasing interest in evaluating cumulative measures, in particular, the availability over a given period. In highly available systems, steady state measures can be very poor, even if the mission time is not small. The use of expectations also suffers from similar drawbacks. Considering, for instance, critical applications, it is crucial for the user to ensure that the probability that its system will achieve a given availability level is high enough. This paper deals with the computation of the distribution of the interval availability which is defined by the fraction of time during which a system is in operation over a finite observation period.Formally, the system is modeled by a Markov process whose state space is divided into the subset of up states and the subset of down states. The interval availability over (0, t) is then the fraction of the interval (0, t) during which the process is in the up states. This random variable has been studied in previous papers as for instance in [l] where its distribution is calculated recursively by discretizing the observation period (0, t). However, no error bounds were found for this approximation method. [4]. Its space complexity depends strongly on the number of operational states. This is of interest especially when this number is small with respect to the size of the whole state space. Another important characteristic of the new algorithm is that it applies for a large class of processes with an infinite state space provided that either the number of up states or the number of down states is finite.The remainder of the paper is organized as follows. In the following section, we describe Algorithm II of [4], which is the basis of the new proposed method. In Section III, we present the new algorithm, we illustrate its performance through a finite state space example and we compare it with Algorithm II of [4]. In S...

International audienceThis survey gives a comprehensive review of recent advances related to the topic of VoIP QoE (Quality of user' Experience). It starts by providing some insight into the QoE arena and outlines the principal building blocks of a VoIP application. The sources of impairments over data IP networks are identified and distinguished from signal-oriented sources of quality degradation observed over telecom networks. An overview of existing subjective and objective methodologies for the assessment of the QoE of voice conversations is then presented outlining how subjective and objective speech quality methodologies have evolved to consider time-varying QoS transport networks. A description of practical procedures for measuring VoIP QoE and illustrative results is then given. Utilization methodology of several speech quality assessment frameworks is summarized. A survey of emerging single-ended parametric-model speech quality assessment algorithms dedicated to VoIP service is then given. In particular, after presenting a primitive single-ended parametric-model algorithm especially conceived for the evaluation of VoIP conversations, new artificial assessors of VoIP service are detailed. In particular, we describe speech quality assessment algorithms that consider, among others, packet loss burstiness, unequal importance of speech wave, and transient loss of connectivity. The following section concentrates on the integration of VoIP service over mobile data networks. The impact of quality-affecting phenomena, such as handovers and CODEC changeover are enumerated and some primary subjective results are summarized. The survey concludes with a review of open issues relating to automatic assessment of VoIP

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