Service oriented networking for multimedia applications in broadband wireless networks

Extensive efforts have been focused on deploying broadband wireless networks. Providing mobile users with high speed network connectivity will let them run various multimedia applications on their wireless devices. In order to successfully deploy and operate broadband wireless networks, it is crucial to design efficient methods for supporting various services and applications in broadband wireless networks. Moreover, the existing access-oriented networking solutions are not able to fully address all the issues of supporting various applications with different quality of service requirements. Thus, service-oriented networking has been recently proposed and has gained much attention. This dissertation discusses the challenges and possible solutions for supporting multimedia applications in broadband wireless networks. The service requirements of different multimedia applications such as video streaming and Voice over IP (VoIP) are studied and some novel service-oriented networking solutions for supporting these applications in broadband wireless networks are proposed. The performance of these solutions is examined in WiMAX networks which are the promising technology for broadband wireless access in the near future. WiMAX networks are based on the IEEE 802.16 standards which have defined different Quality of Service (QoS) classes to support a broad range of applications with varying service requirements to mobile and stationary users. The growth of multimedia traffic that requires special quality of service from the network will impose new constraints on network designers who should wisely allocate the limited resources to users based on their required quality of service. An efficient resource management and network design depends upon gaining accurate information about the traffic profile of user applications. In this dissertation, the access level traffic profile of VoIP applications are studied first, and then a realistic distribution model for VoIP traffic is proposed. Based on this model, an algorithm to allocate resources for VoIP applications in WiMAX networks is investigated. Later, the challenges and possible solutions for transmitting MPEG video streams in wireless networks are discussed. The MPEG traffic model adopted by the WiMAX Forum is introduced and different application-oriented solutions for enhancing the performance of wireless networks with respect to MPEG video streaming applications are explained. An analytical framework to verify the performance of the proposed solutions is discoursed, and it is shown that the proposed solutions will improve the efficiency of VoIP applications and the quality of streaming applications over wireless networks. Finally, conclusions are drawn and future works are discussed

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

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

Video traffic modeling and delivery

Video is becoming a major component of the network traffic, and thus there has been a great interest to model video traffic. It is known that video traffic possesses short range dependence (SRD) and long range dependence (LRD) properties, which can drastically affect network performance. By decomposing a video sequence into three parts, according to its motion activity, Markov-modulated self-similar process model is first proposed to capture autocorrelation function (ACF) characteristics of MPEG video traffic. Furthermore, generalized Beta distribution is proposed to model the probability density functions (PDFs) of MPEG video traffic. It is observed that the ACF of MPEG video traffic fluctuates around three envelopes, reflecting the fact that different coding methods reduce the data dependency by different amount. This observation has led to a more accurate model, structurally modulated self-similar process model, which captures the ACF of the traffic, both SRD and LRD, by exploiting the MPEG structure. This model is subsequently simplified by simply modulating three self-similar processes, resulting in a much simpler model having the same accuracy as the structurally modulated self-similar process model. To justify the validity of the proposed models for video transmission, the cell loss ratios (CLRs) of a server with a limited buffer size driven by the empirical trace are compared to those driven by the proposed models. The differences are within one order, which are hardly achievable by other models, even for the case of JPEG video traffic. In the second part of this dissertation, two dynamic bandwidth allocation algorithms are proposed for pre-recorded and real-time video delivery, respectively. One is based on scene change identification, and the other is based on frame differences. The proposed algorithms can increase the bandwidth utilization by a factor of two to five, as compared to the constant bit rate (CBR) service using peak rate assignment

Self-similarity and stationarity of increments in VBR video

AbstractAs self-similarity trail is being detected in many types of traffic, and the Markovian models failing to represent some statistical behaviors, the tools being used for traffic testing are still complex. Our study here is related to VBR video. Its self-similarity and long-range dependence aspects will be tested using a wavelet-based tool. As the test tool requires stationarity of the increments of the traces, a novel testing technique will be suggested for this aim. Then, the degree of self-similarity will be related to both the traces time scale and its statistical measures of spreading

Modelling of self-similar teletraffic for simulation

Recent studies of real teletraffic data in modern computer networks have shown that teletraffic exhibits self-similar (or fractal) properties over a wide range of time scales. The properties of self-similar teletraffic are very different from the traditional models of teletraffic based on Poisson, Markov-modulated Poisson, and related processes. The use of traditional models in networks characterised by self-similar processes can lead to incorrect conclusions about the performance of analysed networks. These include serious over-estimations of the performance of computer networks, insufficient allocation of communication and data processing resources, and difficulties ensuring the quality of service expected by network users. Thus, full understanding of the self-similar nature in teletraffic is an important issue. Due to the growing complexity of modern telecommunication networks, simulation has become the only feasible paradigm for their performance evaluation. In this thesis, we make some contributions to discrete-event simulation of networks with strongly-dependent, self-similar teletraffic. First, we have evaluated the most commonly used methods for estimating the self-similarity parameter H using appropriately long sequences of data. After assessing properties of available H estimators, we identified the most efficient estimators for practical studies of self-similarity. Next, the generation of arbitrarily long sequences of pseudo-random numbers possessing specific stochastic properties was considered. Various generators of pseudo-random self-similar sequences have been proposed. They differ in computational complexity and accuracy of the self-similar sequences they generate. In this thesis, we propose two new generators of self-similar teletraffic: (i) a generator based on Fractional Gaussian Noise and Daubechies Wavelets (FGN-DW), that is one of the fastest and the most accurate generators so far proposed; and (ii) a generator based on the Successive Random Addition (SRA) algorithm. Our comparative study of sequential and fixed-length self-similar pseudo-random teletraffic generators showed that the FFT, FGN-DW and SRP-FGN generators are the most efficient, both in the sense of accuracy and speed. To conduct simulation studies of telecommunication networks, self-similar processes often need to be transformed into suitable self-similar processes with arbitrary marginal distributions. Thus, the next problem addressed was how well the self-similarity and autocorrelation function of an original self-similar process are preserved when the self-similar sequences are converted into suitable self-similar processes with arbitrary marginal distributions. We also show how pseudo-random self-similar sequences can be applied to produce a model of teletraffic associated with the transmission of VBR JPEG /MPEG video. A combined gamma/Pareto model based on the application of the FGN-DW generator was used to synthesise VBR JPEG /MPEG video traffic. Finally, effects of self-similarity on the behaviour of queueing systems have been investigated. Using M/M/1/∞ as a reference queueing system with no long-range dependence, we have investigated how self-similarity and long-range dependence in arrival processes affect the length of sequential simulations being executed for obtaining steady-state results with the required level of statistical error. Our results show that the finite buffer overflow probability of a queueing system with self-similar input is much greater than the equivalent queueing system with Poisson or a short-range dependent input process, and that the overflow probability increases as the self-similarity parameter approaches one

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization