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

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

Maximizing the number of users in an interactive video-on-demand system

Video prefetching is a technique that has been proposed for the transmission of variable-bit-rate (VBR) videos over packet-switched networks. The objective of these protocols is to prefetch future frames at the customers' set-top box (STB) during light load periods. Experimental results have shown that video prefetching is very effective and it achieves much higher network utilization (and potentially larger number of simultaneous connections) than the traditional video smoothing schemes. The previously proposed prefetching algorithms, however, can only be efficiently implemented when there is one centralized server. In a distributed environment there is a large degradation in their performance. In this paper we introduce a new scheme that utilizes smoothing along with prefetching, to overcome the problem of distributed prefetching. We will show that our scheme performs almost as well as the centralized prefetching protocol even though it is implemented in a distributed environment. In addition, we will introduce a call admission control algorithm for a fully interactive Video-on-Demand (VoD) system that utilizes this concept of distributed video prefetching. Using the theory of effective bandwidths, we will develop an admission control algorithm for new requests, based on the user's viewing behavior and the required Quality of Service (QoS).published_or_final_versio

A new charging scheme for ATM based on QoS

PhDNew services are emerging rapidly within the world of telecommunications. Charging strategies that were appropriate for individual transfer capabilities are no longer appropriate for an integrated broadband communications network. There is currently a range of technologies (such as cable television, telephony and narrow band ISDN) for the different services in use and a limited number of charging schemes are applicable for each of the underlying technologies irrespective of the services used over it. Difficulties arise when a wide range of services has to be supported on the same integrated technology such as asynchronous transfer mode (ATM); in such cases the type of service in use and the impact it has on the network becomes much more important. The subject of this thesis, therefore, is the charging strategies for integrated broadband communications networks. That is, the identification of the requirements associated with ATM charging schemes and the proposal of a new approach to charging for ATM called the “quality of service based charging scheme”. Charging for ATM is influenced by three important components: the type and content of a service being offered; the type of customer using the services; and the traffic characteristics belonging to the application supporting the services. The first two issues will largely be dependent on the business and regulatory requirements of the operators. The last item, and an essential one for ATM, is the bridge between technology and business; how are the resources used by a service quantified? Charging that is based on resource usage at the network level was the prime focus of the research reported here. With the proposed charging scheme, a distinction is first made between the four different ATM transfer capabilities that will support various services and the different quality of service requirements that may be applicable to each of them. Then, resources are distributed among buffers set-up to support the combination of these transfer capabilities and quality of services. The buffers are dimensioned according to the M/D/1/K and the ND/D/1 queuing analysis to determine the buffer efficiency and quality of service requirements. This dimensioning provides the basis for fixing the price per unit of resource and time. The actual resource used by a connection is based on the volume of cells transmitted or peak cell rate allocation in combination with traffic shapers if appropriate. Shapers are also dimensioned using the quality of service parameters. Since the buffer 4 efficiency is dependent on the quality of service requirements, users (customers) of ATM networks buy quality of service. The actual price of a connection is further subjected to a number of transformations based on the size of the resource purchased, the time of the day at which a connection is made, and the geographical locality of the destination switch. It is demonstrated that the proposed charging scheme meets all the requirements of customers and of network operators. In addition the result of the comparison of the new scheme with a number of existing, prominent, ATM charging schemes is presented, showing that the performance of the proposed scheme is better in terms of meeting the expectations of both the customers and the network operators

Protecting video service quality in multimedia access networks through PCN

The growing popularity of video-based services and their corresponding unpredictable bursty behavior makes the design of an admission control system an important research challenge. The pre-congestion notification (PCN) mechanism is a measurement-based approach, recently standardized by the IETF, optimized toward the admission of inelastic flows, where the number of flows is sufficiently large that individual bursts of flows can be compensated by silence periods of others. In this article, we discuss the implications of applying PCN to protect video services, which have less predictable behavior. Several algorithms for protecting video services in multimedia access networks are described. Through performance evaluation, we show the impact of these algorithms on the network utilization and video quality, and present guidelines on how to configure a PCN system

Video Smoothing of Aggregates of Streams with Bandwidth Constraints

Compressed variable bit rate (VBR) video transmission is acquiring a growing importance in the telecommunication world. High data rate variability of compressed video over multiple time scales makes an efficient bandwidth resource utilization difficult to obtain. One of the approaches developed to face this problem are smoothing techniques. Various smoothing algorithms that exploit client buffers have been proposed, thus reducing the peak rate and high rate variability by efficiently scheduling the video data to be transmitted over the network. The novel smoothing algorithm proposed in this paper, which represents a significant improvements over the existing methods, performs data scheduling both for a single stream and for stream aggregations, by taking into account available bandwidth constraints. It modifies, whenever possible, the smoothing schedule in such a way as to eliminate frame losses due to available bandwidth limitations. This technique can be applied to any smoothing algorithm already present in literature and can be usefully exploited to minimize losses in multiplexed stream scenarios, like Terrestrial Digital Video Broadcasting (DVB-T), where a specific known available bandwidth must be shared by several multimedia flows. The developed algorithm has been exploited for smoothing stored video, although it can also be quite easily adapted for real time smoothing. The obtained numerical results, compared with the MVBA, another smoothing algorithm that is already presented and discussed in literature, show the effectiveness of the proposed algorithm, in terms of lost video frames, for different multiplexed scenarios

Quality of service over ATM networks

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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)

Variable bit rate video time-series and scene modeling using discrete-time statistically self-similar systems

This thesis investigates the application of discrete-time statistically self-similar (DTSS) systems to modeling of variable bit rate (VBR) video traffic data. The work is motivated by the fact that while VBR video has been characterized as self-similar by various researchers, models based on self-similarity considerations have not been previously studied. Given the relationship between self-similarity and long-range dependence the potential for using DTSS model in applications involving modeling of VBR MPEG video traffic data is presented. This thesis initially explores the characteristic properties of the model and then establishes relationships between the discrete-time self-similar model and fractional order transfer function systems. Using white noise as the input, the modeling approach is presented using least-square fitting technique of the output autocorrelations to the correlations of various VBR video trace sequences. This measure is used to compare the model performance with the performance of other existing models such as Markovian, long-range dependent and M/G/(infinity) . The study shows that using heavy-tailed inputs the output of these models can be used to match both the scene time-series correlations as well as scene density functions. Furthermore, the discrete-time self-similar model is applied to scene classification in VBR MPEG video to provide a demonstration of potential application of discrete-time self-similar models in modeling self-similar and long-range dependent data. Simulation results have shown that the proposed modeling technique is indeed a better approach than several earlier approaches and finds application is areas such as automatic scene classification, estimation of motion intensity and metadata generation for MPEG-7 applications