Abstract—Due to the explosive growth of the Internet and increasing demand for multimedia information on the web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. To address these challenges, extensive research has been conducted. This special issue is aimed at dissemination of the contributions in the field of streaming video over the Internet. To introduce this special issue with the necessary background and provide an integral view on this field, we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions. Index Terms—Application-layer QoS control, continuous media distribution services, Internet, protocol, streaming video,

Abstract—Reservation and adaptation are two well-known and effective techniques for enhancing the end-to-end performance of network applications. However, both techniques also have limitations, particularly when dealing with high-bandwidth, dynamic flows: fixed-capability reservations tend to be wasteful of resources and hinder graceful degradation in the face of congestion, while adaptive techniques fail when congestion becomes excessive. We propose an approach to quality of service (QoS) that overcomes these difficulties by combining features of reservations and adaptation. In this approach, a combination of online control interfaces for resource management, a sensor permitting online monitoring, and decision procedures embedded in resources enable a rich variety of dynamic feedback interactions between applications and resources. We describe a QoS architecture, GARA, that has been extended to support these mechanisms, and use three examples of application-level adaptive strategies to show how this framework can permit applications to adapt both their resource requests and behavior in response to online sensor information. I.

The Differentiated Services framework (DiffServ) has been proposed to provide multiple Quality of Service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. We propose a pricing scheme in a DiffServ environment based on the cost of providing different levels of quality of service to different classes, and on long-term demand. Pricing of network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service contract according to network conditions. We develop a DiffServ simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive service negotiation to that of a network with a static pricing policy. Adaptive users adapt to price changes by adjusting their sending rate or selecting a different service class. We also develop the demand behavior of adaptive users based on a perceptually reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allow a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure. Allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads. I.

Network delivery services providing “better-than-best-effort ” service over the Internet are being studied, and are particularly necessary for multimedia applications. The selection and use of a specific delivery service involves negotiation between the user and the network; they agree upon specifications such as the type of service user packets will receive, the constraints the user traffic must adhere to, and the price to be charged for the service. In this paper, we describe a protocol through which the user and the network (or two network domains) can negotiate network services. We refer to the protocol as a Resource Negotiation and Pricing protocol (RNAP). Through RNAP, the network service provider communicates availability of services and delivers price quotations and charging information to the user, and the user requests or re-negotiates services with desired specifications for one or more flows. RNAP protocol mechanisms are flexible enough to support multiple delivery service models, and allow dynamic re-negotiation of services during a session. Two different network architectures are defined to support RNAP, a centralized architecture with a Network Resource Negotiator (NRN) administering each network domain, and a distributed architecture without any centralized controlling entity. Mechanisms are proposed for local price and charge computation, formulation of end-to-end prices and charges across multiple domains, and communication of this information through RNAP messages. Results of a prototype implementation are briefly described. 1

Abstract—We study a dynamic, usage- and congestion-dependent pricing system in conjunction with price-sensitive user adaptation of network usage. We first present a resource negotiation and pricing (RNAP) protocol and architecture to enable users to select and dynamically renegotiate network services. We develop mechanisms within the RNAP architecture for the network to dynamically formulate prices and communicate pricing and charging information to the users. We then outline a general pricing strategy in this context. We discuss candidate algorithms by which applications (singly, or as part of a multiapplication system) can adapt their rate and QoS requests, based on the user-perceived value of a given combination of transmission parameters. Finally, we present experimental results to show that usage- and congestion-dependent pricing can effectively reduce the blocking probability, and allow bandwidth to be shared fairly among applications, depending on the elasticity of their respective bandwidth requirements. Index Terms—Adaptive systems, communication system economics, communication system signaling, communication system traffic, computer network management, multimedia communication, resource management. I.

In open and heterogeneous environments, where an unpredictable number of applications compete for a limited amount of resources, executions can be affected by also unpredictable delays, which may not even be bounded. Since many of these applications have timeliness requirements, they can only be implemented if they are able to adapt to the existing conditions. Adaptation can be done by several ways, taking into account many different factors, but an obvious factor of success is knowing what they have to adapt to. In this paper we present a novel approach, called Dependable QoS adaptation, which can only be achieved if the environment is accurately and reliably observed. Dependable

Abstract — The rapid deployment of new applications and the inter-connection of networks with increasing diversity of technologies and capacity make it more challenging to provide end-to-end quality assurance to the value-added services, such as the transmission of real-time multimedia and mission critical data. In a network with enhancements for QoS support, pricing of network services based on the level of service, usage, and congestion provides a natural and equitable incentive for multimedia applications to adapt their sending rates according to network conditions. We have developed an intelligent service architecture that integrates resource reservation, negotiation, pricing and adaptation in a flexible and scalable way. In this paper, we present a generic pricing structure that characterizes the pricing schemes widely used in the current Internet, and introduce a dynamic, congestion-sensitive pricing algorithm that can be used with the proposed service

Choice of mechanisms for disseminating multimedia contents to geographically dispersed participants is influenced by many factors including network topology, encoding technique, service model, and mode of transmission. Encoding techniques render the multimedia file sizes manageable while a multicast service model provides a scalable architecture to disseminate the contents. The underlying network can be a private Intranet, Internet, or heterogeneous which can be a combination of several network topologies. The mode of transmission can be, synchronous, when live or recorded contents are transmitted to multiple users simultaneously or on-demand, when the files are served as per users’ requests. We measure effectiveness of delivery by the Quality of Service (QoS) experienced by end users. Efficiency is measured in terms of optimal utilization of system and network resources in delivering contents. Given a specific encoding technique and a multicast service model, mechanisms for effective and efficient delivery of multimedia contents vary depending on the nature of the underlying network topology and the mode of transmission deployed.

High bandwidth requirements in multimedia transmission make the efficient use of limited network resource a challenging task, especially when multiple clients make their requests to the server simultaneously. In this paper, we propose a self-adjusted network transmission mechanism for multiple clients. Instead of assigning a fixed bandwidth for each client, the server determines the transmission rate for each client connection according to the buffer packets and playback rate at each client. Transmission rates are adjusted when the total requested bandwidth is larger than the network bandwidth. In addition, the proposed mechanism can minimize the bandwidth allocation and maximize the client buffer utilization. A simulation is performed and the simulation results show that the proposed mechanism can dynamically change the transmission rate for each client to avoid overflow of the client buffer, and achieve the optimal utilization of the limited network resource in multiple client network environments.

Providing quality of service (QoS) guarantees is an important objective in the design of the next-generation wireless networks. In this dissertation, we address the QoS provisioning problem from both the network and the end system perspectives. In the first