A genetic approach to Markovian characterisation of H.264 scalable video

We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios

Quantifying the impact of daily and seasonal variation in sap pH on xylem dissolved inorganic carbon estimates in plum trees

In studies on internal CO2 transport, average xylem sap pH (pH(x)) is one of the factors used for calculation of the concentration of dissolved inorganic carbon in the xylem sap ([CO2*]). Lack of detailed pH(x) measurements at high temporal resolution could be a potential source of error when evaluating [CO2*] dynamics. In this experiment, we performed continuous measurements of CO2 concentration ([CO2]) and stem temperature (T-stem), complemented with pH(x) measurements at 30-min intervals during the day at various stages of the growing season (Day of the Year (DOY): 86 (late winter), 128 (mid-spring) and 155 (early summer)) on a plum tree (Prunus domestica L. cv. Reine Claude d'Oullins). We used the recorded pH(x) to calculate [CO2*] based on T-stem and the corresponding measured [CO2]. No statistically significant difference was found between mean [CO2*] calculated with instantaneous pH(x) and daily average pH(x). However, using an average pH(x) value from a different part of the growing season than the measurements of [CO2] and T-stem to estimate [CO2*] led to a statistically significant error. The error varied between 3.25 +/- 0.01% under-estimation and 3.97 * 0.01% over-estimation, relative to the true [CO2*] data. Measured pH(x) did not show a significant daily variation, unlike [CO2], which increased during the day and declined at night. As the growing season progressed, daily average [CO2] (3.4%, 5.3%, 7.4%) increased and average pH(x) (5.43, 5.29, 5.20) decreased. Increase in [CO2] will increase its solubility in xylem sap according to Henry's law, and the dissociation of [CO2*] will negatively affect pH(x). Our results are the first quantifying the error in [CO2*] due to the interaction between [CO2] and pH(x) on a seasonal time scale. We found significant changes in pH(x) across the growing season, but overall the effect on the calculation of [CO2*] remained within an error range of 4%. However, it is possible that the error could be more substantial for other tree species, particularly if pH(x) is in the more sensitive range (pHx > 6.5)

The pseudo-self-similar traffic model: application and validation

Since the early 1990¿s, a variety of studies has shown that network traffic, both for local- and wide-area networks, has self-similar properties. This led to new approaches in network traffic modelling because most traditional traffic approaches result in the underestimation of performance measures of interest. Instead of developing completely new traffic models, a number of researchers have proposed to adapt traditional traffic modelling approaches to incorporate aspects of self-similarity. The motivation for doing so is the hope to be able to reuse techniques and tools that have been developed in the past and with which experience has been gained. One such approach for a traffic model that incorporates aspects of self-similarity is the so-called pseudo self-similar traffic model. This model is appealing, as it is easy to understand and easily embedded in Markovian performance evaluation studies. In applying this model in a number of cases, we have perceived various problems which we initially thought were particular to these specific cases. However, we recently have been able to show that these problems are fundamental to the pseudo self-similar traffic model. In this paper we review the pseudo self-similar traffic model and discuss its fundamental shortcomings. As far as we know, this is the first paper that discusses these shortcomings formally. We also report on ongoing work to overcome some of these problems

Adaptive CAC for SVC Video Traffic in IEEE 802.16 Networks

International audienceCall Admission Control is a key function that guarantees the Quality of Service (QoS) for users. In radio networks, this function is usually based on traffic models and ensures that sessions are admitted only if the estimated available bandwidth is enough for the entire call duration. For video on IEEE 802.16, the CAC function must ensure that the bandwidth to be reserved is compatible with the resource availability. For the enhanced SVC (Scalable Video Coding) systems, the CAC function must take into account all the layers and their characteristics. In this paper, we propose an enhanced CAC function for SVC that adapts the admission according to the statistical behaviour of the video sessions. The main goal is to use measurements in the 802.16 base station (BS) to update the traffic model of SVC video flows, this for the different layers of SVC flows. We then use the variability of the traffics generated to adapt the CAC according to the characteristics of incoming flows. To perform that, we use a Markovian model that adapts for each flow instead of using a generic static one as used in most of the papers. Performance evaluation is given to illustrate the interest of our proposal

Cross-layer performance control of wireless channels using active local profiles

To optimize performance of applications running over wireless channels state-of-the-art wireless access technologies incorporate a number of channel adaptation mechanisms. While these mechanisms are expected to operate jointly providing the best possible performance for current wireless channel and traffic conditions, their joint effect is often difficult to predict. To control functionality of various channel adaptation mechanisms a new cross-layer performance optimization system is sought. This system should be responsible for exchange of control information between different layers and further optimization of wireless channel performance. In this paper design of the cross-layer performance control system for wireless access technologies with dynamic adaptation of protocol parameters at different layers of the protocol stack is proposed. Functionalities of components of the system are isolated and described in detail. To determine the range of protocol parameters providing the best possible performance for a wide range of channel and arrival statistics the proposed system is analytically analyzed. Particularly, probability distribution functions of the number of lost frames and delay of a frame as functions of first- and second-order wireless channel and arrival statistics, automatic repeat request, forward error correction functionality, protocol data unit size at different layers are derived. Numerical examples illustrating performance of the whole system and its elements are provided. Obtained results demonstrate that the proposed system provide significant performance gains compared to static configuration of protocols

Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94–97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management

Internet traffic modeling by means of Hidden Markov Models

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On the use of Hidden Markov Processes and auto-regressive filters to incorporate indoor bursty wireless channels into network simulation platforms

In this paper we thoroughly analyze two alternatives to replicate the bursty behavior that characterizes real indoor wireless channels within Network Simulation platforms. First, we study the performance of an improved Hidden Markov Process model, based on a time-wise configuration so as to decouple its operation from any particular traffic pattern. We compare it with the behavior of Bursty Error Model Based on an Auto-Regressive Filter, a previous proposal of ours that emulates the received Signal to Noise Ratio by means of an auto-regressive filter that captures the “memory” assessed in real measurements. We also study the performance of one of the legacy approaches intrinsically offered by most network simulation frameworks. By means of a thorough simulation campaign, we demonstrate that our two models are able to offer a much more realistic behavior, yet maintaining an affordable response in terms of computational complexity.The authors would like to express their gratitude to the Spanish government for its funding in the project “Connectivity as a Service: Access for the Internet of the Future”, COSAIF (TEC2012-38574-C02-01