We propose an on-line presentation of iTVP [3, 2, 4, 1], a platform for delivery of digital audio and video, developed and deployed for the Polish national broadcaster. iTVP designed for operation on a large country-wide scale with the potential of serving selected content to any Internet user. The system is built for delivery of the following services: transmissions of live TV programming, time shifting of live transmissions, video-on-demand, audio-on-demand and accompanying services such as Electronic Program Guide and Personal Video Recorder. The platform is designed to deliver content from multiple independent content providers and with cooperation of last mile network operators. Content can be made available in various encoding formats and delivered to a variety of end devices. Content delivery system is one of the key components of the platform which includes also content provider systems, a number of interactive portals serving as primary access points for end users, and license management system. Functionally, the iTVP CDN consists of the following subsystems: distribution, monitoring, management and reporting subsystem.

Providing quality of service (QoS) for Internet-based video streaming applications requires the server and/or client to be network-aware and adaptive. We present a dynamic rate and quality adaptation algorithm where the server varies its sending rate (without varying the quality level) to adapt to the network and client conditions and only as a last resort, does quality adaptation. We place the adaptation logic at the client since it has better knowledge about both the demand (buffer conditions, variable bit-rate requirements) and supply (network conditions). Our approach is unique because the server's sending rate is calculated based on the client's varying demand (consumption rate) and the network status. Also, we do not model the network as a black-box but instead augment endpoint observations with a feedback from the network to represent its status more precisely. To make an informed adaptation decision, the client requires sizes of all frames in the variable bit rate video. But the overhead involved in sending this metadata is significant. So we propose a lossy compression technique to reduce the amount of control information and consequently the overhead. We also present a scheduling algorithm, dynamic scheduling algorithm for reduced trace delivery (DART), to deliver the compressed control information to the client. This algorithm can be used to deliver any form of metadata (like subtitles, alerts, etc.), especially in applications like IP-TV. Simulations show that the proposed techniques can significantly improve user perceived QoS when compared to other popular adaptation methods.

In this paper, we propose a Loopback approach in a two-level streaming architecture to exploit collaborative client/proxy buffers for improving the quality and efficiency of large-scale streaming applications. At the upper level we use a content delivery network (CDN) to deliver video from a central server to proxy servers. At the lower level a proxy server delivers video with the help of collaborative client caches. In particular, a proxy server and its clients in a local domain cache different portions of a video and form delivery loops. In each loop, a single video stream originates at the proxy, passes through a number of clients, and finally is passed back to the proxy. As a result, with limited bandwidth and storage space contributed by collaborative clients, we are able to significantly reduce the required network bandwidth, I/O bandwidth, and cache space of a proxy. Furthermore, we develop a local repair scheme to address the client failure issue for enhancing service quality and eliminating most required repairing load at the central server. For popular videos, our local repair scheme is able to handle most of single-client failures without service disruption and retransmissions from the central server. Our analysis and simulations have shown the effectiveness of the proposed scheme.

In this paper, we propose a hybrid architecture to integrate peer-to-peer (P2P) streaming approaches with content distribution networks (CDNs). We further utilize multiple description (MD) coding in this architecture to address challenging issues in P2P approaches such as low peer upstream bandwidth and low quality assurance. The proposed schemes take advantage of the high availability of CDNs, the flexibility of layered MD, and the high scalability of P2P approaches to better utilize peer upstream bandwidth, reduce server load, and assure service quality. In addition, we evaluate the performance of the proposed approach through analysis and simulation

Video streaming on a large scale can be expensive and resource demanding. Periodic broadcast reduces server I/O bandwidth usage for popular videos. While improving the scalability this mechanism increases considerably the WAN bandwidth usage and buffer space needed by a client. We address the resource demands on the client side with proxy caching. We show that prefix caching can lower WAN bandwidth usage and derive the lower limit, and that chunk caching can reduce buffer space requirements. We introduce periodic broadcast schemes designed to work with both types of caching and analyze their cost

Since the current Internet provides a best-effort service, timely delivery of data is not guaranteed for video streaming applications. Providing quality of service (QoS) in such a setting requires server and client to be network-aware and adaptive. We present a dynamic rate and quality adaptation scheme where the server varies its sending rate (without varying the quality) to adapt to the network and client conditions, and, only as a last resort, does quality adaptation. Our approach is unique because the server's sending rate is calculated based on the client's varying demand, and the network status. We do not model the network as a black-box but instead augment endpoint observations with feedback from the network and hence represent its status more precisely. We also propose a method to reduce the amount of control information needed to make adaptation decisions