In the microprocessor system, the bandwidth problems of instruction stream and data stream are the main causes that limit the performance of the system. Although cache can effectively smooth this problem, the processor still needs more than one clock cycle to get the data. The large hardware cost and power consumption also limit the cache in the embedded system applications. The buffering techniques, such as the loop buffer and the prefetch buffer, can improve the performance in low hardware. Their mechanisms emphasize on the buffering of the continuous data space. For the non-continuous data space accesses caused by the branch instructions, they cannot exploit the reference localities. In this thesis, we propose a new buffering mechanism called as the ABP buffer, which is composed of a buffering mechanism and a prefetching mechanism. The buffering mechanism can effectively buffer the non-continuous data space and replace the buffer lines in a replacement policy, which is suitable for hardware realization. The prefetching mechanism exploits the hit time to prefetch the data that can be used in near future. The simulation and implement results show that the ABP buffer can gain high performance in low hardware and the control parts of the mechanism only occupy 4% of the total hardware.

Delivering high-quality of video to end users over the best-effort Internet is a challenging task since quality of streaming video is highly subject to network conditions. A fundamental issue in this area is how real-time applications cope with network dynamics and adapt their operational behavior to offer a favorable streaming environment to end users. As an effort towards providing such streaming environment, the first half of this work focuses on analyzing the performance of video streaming in best-effort networks and developing a new streaming framework that effectively utilizes unequal importance of video packets in rate control and achieves a near-optimal performance for a given network packet loss rate. In addition, we study error concealment methods such as FEC (Forward-Error Correction) that is often used to protect multimedia data over lossy network channels. We investigate the impact of FEC on the quality of video and develop models that can provide insights into understanding how inclusion of FEC affects streaming performance and its optimality and resilience characteristics under dynamically changing network conditions. In the second part of this thesis, we focus on measuring bandwidth of network paths, which plays an important role in characterizing Internet paths and can benefit many applications including multimedia streaming. We conduct a stochastic analysis of an end-to-end path and develop novel bandwidth sampling techniques that can produce asymptotically accurate capacity and available bandwidth of the path under non-trivial cross-traffic conditions. In addition, we conduct comparative performance study of existing bandwidth estimation tools in non-simulated networks where various timing irregularities affect delay measurements. We find that when high-precision packet timing is not available due to hardware interrupt moderation, the majority of existing algorithms are not robust to measure end-to-end paths with high accuracy. We overcome this problem by using signal de-noising techniques in bandwidth measurement. We also develop a new measurement tool called PRC-MT based on theoretical models that simultaneously measures the capacity and available bandwidth of the tight link with asymptotic accuracy.

A heuristic approach to the application of bandwidth-oriented signal coordination is proposed based on a system partition technique. The proposed approach divides a large signalized arterial into subsystems based on clustering results considering factors such as block distance and turning movements. Each subsystem is optimized to achieve the maximum bandwidth efficiency. Evaluation of the system includes two parts, THOS (through opportunity) comparison and simulation evaluation. Two case studies are presented to illustrate how the proposed approach can be applied, and the influence of clustering method on signal coordination is presented with comparison of three scenarios, no partition, 2 clusters and 3 clusters. Evaluation of the case study shows that clustering method is beneficial in improving progression bandwidth, bandwidth efficiency, bandwidth attainability and THOS. Clustering is good for signal coordination in that either 2 clusters or 3 clusters will result in better performance measures that no partition. However, clustering is not always good for signal coordination in certain conditions. Though bandwidth and bandwidth efficiency of each sub-system can be improved after partition, control delay or number of stops for the corridor might be increased instead for certain conditions of the entire corridor. Whether or not clustering method can be used to partition a signalized system for the purpose of better signal coordination depends on specific traffic and geometric conditions of the corridor. When bandwidth capacity is exceeded by demand, bandwidth optimization should better give way to delay-based optimization strategies.

Broadband Wireless Access Systems (BWASs) such as High Speed Downlink Packet Access (HSDPA) and the Worldwide Interoperability for Microwave Access (WiMAX), pose a myriad of new opportunities for leveraging the support of a wide range of “content-rich” mobile multimedia services with diverse Quality of Service (QoS) requirements. This is due to the remarkably high bandwidth that is supported by these systems, which was previously only available to wireline connections. Despite the support for such high bandwidth, satisfying the diverse QoS of users while maximizing the revenues of network operators is still one of the major issues in these systems. Bandwidth management, therefore, will play a decisive role in the success of such wireless access systems. Without efficient bandwidth management, network operators may not be able to meet the growing demand of users for multimedia services, and may consequently suffer great revenue loss. Bandwidth management in BWASs is, however, a challenging problem due to many issues that need to be taken into consideration. Examples of such issues include the diverse QoS requirements of the services that BWASs support, the varying channel quality conditions of mobile users, and hence the varying amount of resources that are needed to guarantee certain QoS levels during the lifetime of user connections, the utilization of shared channels for data delivery instead of dedicated ones and network congestion. In this thesis, we address the problem of bandwidth management in BWASs and propose efficient economic-based solutions in order to deal with these issues at different bandwidth management levels, and hence enhance the QoS support in these systems. Specifically, we propose a bandwidth management framework for BWASs. The framework is designed to support multiple classes of traffic with different users having different QoS requirements, maximize the throughput of BWASs, support inter- and intra-class fairness, prevent network congestion and maximize the network operator’s revenues. The framework consists of three related components, namely packet scheduling, bandwidth provisioning and Call Admission Control-based dynamic pricing. By efficiently managing the wireless bandwidth prior to users’ admission (i.e.,pre-admission bandwidth management) and during the users’ connections (i.e., post-admission bandwidth management), these schemes are shown to achieve the design goals of our framework. / Thesis (Ph.D, Computing) -- Queen's University, 2009-04-01 15:35:36.213

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The study focuses on building a financial model for a telecommunications carrier to guide it towards profitable network investments. The model shows optimal access-network topography by comparing two broadband delivery techniques over fibre technology. The study is a scenario exploration of how a large telecommunication company deploying fibre will see its investment pay off in a Canadian residential market where cable operators are using competing technology serving the same bandwidth hungry consumers. The comparison is made at the last mile by studying how household densities, bandwidth demand, competition, geographic and deployment considerations affect the economics of fibre technology investment. Case comparisons are made using custom models that extend market forecasts to estimate future bandwidth demand. Market uptake is forecasted using sigmoid curves in an environment where competing and older technologies exist. Sensitivity analyses are performed on each fibre technology to assess venture profitability under different scenarios.

The study focuses on building a financial model for a telecommunications carrier to guide it towards profitable network investments. The model shows optimal access-network topography by comparing two broadband delivery techniques over fibre technology. The study is a scenario exploration of how a large telecommunication company deploying fibre will see its investment pay off in a Canadian residential market where cable operators are using competing technology serving the same bandwidth hungry consumers. The comparison is made at the last mile by studying how household densities, bandwidth demand, competition, geographic and deployment considerations affect the economics of fibre technology investment. Case comparisons are made using custom models that extend market forecasts to estimate future bandwidth demand. Market uptake is forecasted using sigmoid curves in an environment where competing and older technologies exist. Sensitivity analyses are performed on each fibre technology to assess venture profitability under different scenarios.

Ever since the pioneering work of Parzen the mean square error( MSE) and its integrated form (MISE) have been used as the error criteria in choosing the bandwidth matrix for multivariate kernel density estimation. More recently other criteria have been advocated as competitors to the MISE, such as the mean absolute error. In this study we define a weighted version of the Hellinger distance for multivariate densities and show that it has an asymptotic form, which is one-fourth the asymptotic MISE under weak smoothness conditions on the multivariate density f. In addition the proposed criteria give rise to a new data-dependent bandwidth matrix selector. The performance of the new data-dependent bandwidth matrix selector is compared with other well known bandwidth matrix selectors such as the least squared cross validation (LSCV) and the plug-in (HPI) through simulation. We derived a closed form formula for the mean Hellinger distance (MHD) in the univariate case. We also compared via simulation mean weighted Hellinger distance (MWHD) and the asymptotic MWHD, and the MISE and the asymptotic MISE for both univariate and bivariate cases for various densities and sample sizes.

Video streaming over the best-effort networks is a challenging problem due to the time-varying and uncertain characteristics of the links. When multiple video streams are present in a network, they share and compete for the common bandwidth. In such a setting, a bandwidth allocation algorithm is required to distribute the available resources among the streams in a fair and efficient way. Specifically, it is desired to establish fairness across end-users' Quality of Experience (QoE). In this research, we propose three novel methods to provide QoE-fair network bandwidth allocation among multiple video streaming sessions. First, we formulate the problem of bandwidth allocation for video flows in the context of Network Utility Maximization (NUM) framework, using sigmoidal utility functions, rather than conventional but unrealistic concave functions. An approximation algorithm for Sigmoidal Programming (SP) is utilized to solve the resulting nonconvex optimization problem, called NUM-SP. Simulation results indicate improvements of at least 60% in average utility/QoE and 45% in fairness, while using slightly less network resources, compared to two representative methods. Subsequently, we take a collaborative decision-theoretic approach to the problem of rate adaptation among multiple video streaming sessions, and design a multi-objective foresighted optimization model for network resource allocation. A social welfare function is constructed to capture both fairness and efficiency objectives at the same time. Then, assuming a common altruistic goal for all network users, we use multi-agent decision processes to find the optimal policies for all players. We propose a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model for the conventional IP networks and a Multi-agent Markov Decision Process (MMDP) model for the SDN-enabled wireless networks. By planning these cooperative decision process models, we find the optimal network bandwidth allocation that leads to social welfare maximization. Distributed multi-agent reinforcement learning algorithms are also designed and proposed as a low-complexity model-free solution to these optimization problems. Simulations of the proposed methods show that the resulting optimal policies of the novel Social Utility Maximization (SUM) framework outperform existing approaches in terms of both efficiency and fairness. The Dec-POMDP model applied to a server-side rate adaptation results in 25% improvement in efficiency and 13% improvement in fairness, compared to one popular protocol of congestion control for multimedia streaming. Our performance evaluations also show that the MMDP model applied to a client-side rate adaptation like DASH improves efficiency, fairness, and social welfare by as much as 18%, 24%, and 25%, respectively compared to current state-of-the-art.