P2P file downloading and streaming have already become very popular Internet applications. These systems dramatically reduce the server loading, and provide a platform for scalable content distribution, as long as there is interest for the content. P2P-based video-on-demand (P2P-VoD) is a new challenge for the P2P technology. Unlike streaming live content, P2P-VoD has less synchrony in the users sharing video content, therefore it is much more difficult to alleviate the server loading and at the same time maintaining the streaming performance. To compensate, a small storage is contributed by every peer, and new mechanisms for coordinating content replication, content discovery, and peer scheduling are carefully designed. In this paper, we describe and discuss the challenges and the architectural design issues of a large-scale P2P-VoD system based on the experiences of a real system deployed by PPLive. The system is also designed and instrumented with monitoring capability to measure both system and component specific performance metrics (for design improvements) as well as user satisfaction. After analyzing a large amount of collected data, we present a number of results on user behavior, various system performance metrics, including user satisfaction, and discuss what we observe based on the system design. The study of a real life system provides valuable insights for the future development of P2P-VoD technology.

Live peer-to-peer (P2P) streaming applications have been successfully deployed in the Internet. With relatively simple peer selection protocol design, modern live P2P streaming applications are able to provide millions of concurrent users adequately satisfying viewing experiences. That said, few existing research has provided sufficient insights on the time-varying internal characteristics of P2P topologies in live streaming. With 120 GB worth of traces in late 2006 from a commercial P2P live streaming system of UUSee Inc. in Beijing, this paper represents the first attempt in the research community to explore topological properties in practical P2P streaming, and how they behave over time. Starting from classical graph metrics, such as degree, clustering coefficient, and reciprocity, we explore and extend them in specific perspectives of streaming applications. We also compare our findings with existing insights from topological studies of P2P file sharing applications, which shed new and unique insights specific to streaming. Our characterization reveals the scalability of the commercial P2P streaming application even in case of large flash crowds, the clustering phenomenon of peers in each ISP, as well as the reciprocal behavior among peers, all of which play important roles in achieving its current success. 1

P2P systems exploit the uploading bandwidth of individual peers to distribute content at low server cost. While the P2P bandwidth sharing design is very efficient for bandwidth sensitive applications, it imposes a fundamental performance constraint for delay sensitive applications: the uploading bandwidth of a peer cannot be utilized to upload a piece of content until it completes the download of that content. This constraint sets up a limit on how fast a piece of content can be disseminated to all peers in a P2P system. In this paper, we theoretically study the impact of this inherent delay constraint and derive the minimum delay bounds for realtime P2P streaming systems. We show that the bandwidth heterogeneity among peers can be exploited to significantly improve the delay performance of all peers. We further propose a simple snow-ball streaming algorithm to approach the minimum delay bound in realtime P2P video streaming. Our analysis suggests that the proposed algorithm has better delay performance and more robust than existing tree-based streaming solutions. Insights brought forth by our study can be used to guide the design of new P2P systems with shorter startup delays.

IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service. In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to large group. In this paper, we address the design of overlay which achieves low source-to-peer delay, is robust to user churn, accommodates of asymmetric and diverse uplink bandwidth, and continuously improves based on existing user pool. A natural choice is the use of mesh, where each peer is served by multiple parents. Since the peer delay in a mesh depends on its longest path through its parents, we study how to optimize such delay while meeting a certain streaming rate requirement. We first formulate the minimum delay mesh problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge which serves as our benchmark and optimal solution for all the other schemes under comparison. Our heuristic makes use of the concept of power in network given by the ratio of throughput and delay. By maximizing the network power, our heuristic achieves very low delay. We then propose a simple distributed algorithm where peers select their parents based on the power concept. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation results show that our distributed protocol performs close to the centralized one, and substantially outperforms traditional and state-of-the-art approaches.

We consider the design of an open P2P live-video streaming system. When designing a live video system that is both open and P2P, the system must include mechanisms that incentivize peers to contribute upload capacity. We advocate an incentive principle for live P2P streaming: a peer’s video quality is commensurate with its upload rate. We propose Substream Trading, a new P2P streaming design which not only enables differentiated video quality commensurate with a peer’s upload contribution but can also accommodate different video coding schemes, including single-layer coding, layer coding, and multiple description coding. Extensive trace-driven simulations show that substream trading has high efficiency, provides differentiated service, low start-up latency, synergies among peers with different Internet access rates, and protection against freeriders. 1.

This paper presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system which is arguably the most popular and largest multimedia streaming p2p system today. We believe that our findings can be used to understand large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of channel population size and session length. Our studies also reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) Small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure, (2) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel population size, (3) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing users, PPLive peers are impatient, (5) Session lengths (discretized, per channel) are typically geometrically distributed, (6) Channel Population Size variations are larger than in p2p file-sharing networks, yet they can be fitted with polynomial mathematical models. We conclude with a series of suggestions on how our findings can improve IPTV future design. 1.

P2P mesh-pull live video streaming applications – such as Cool-Streaming, PPLive, and PPStream – have become popular in the recent years. In this paper, we examine the stream pollution attack, for which the attacker mixes polluted chunks into the P2P distribution, degrading the quality of the rendered media at the receivers. Polluted chunks received by an unsuspecting peer not only effect that single peer, but since the peer also forwards chunks to other peers, and those peers in turn forward chunks to more peers, the polluted content can potentially spread through much of the P2P network. The contribution of this paper is twofold. First, by way of experimenting and measuring a popular P2P live video streaming system, we show that the pollution attack can be devastating. Second, we evaluate the applicability of four possible defenses to the pollution attack: blacklisting, traffic encryption, hash verification, and chunk signing. Among these, we conclude that the chunk signing solutions are most suitable. 1.

... revolutionize the entertainment and media industries; however, IPTV has the potentials to overwhelm the Internet backbone and access networks with traffic. To date, IPTV over P2P streaming networks have advanced significantly in two major approaches: tree-push vs. mesh-pull. In particular, the meshpull streaming approach has achieved a number of successful commercial deployments. In this article, we examine the current progress on the research and development of mesh-pull P2P streaming systems. We provide an overview of the general meshpull streaming architecture, review various challenges, design issues, and interesting research problems in this approach. We discuss the construction cost to provide an IPTV service with service guarantees. We outline a measurement technique for monitoring the video playback quality of mesh-pull streaming systems. We emphasize that the future P2P IPTV systems should be designed towards users’ Quality-of-Experience (QoE). We also identify a few other important issues for IPTV over P2P streaming networks, including traffic pressure on ISPs, various security concerns and the necessity of re-examination of the most appropriate P2P architecture. Insights obtained in this study will be valuable for the development and deployment of future P2P IPTV systems.

Understanding the channel popularity or content popularity is an important step in the workload characterization for modern information distribution systems (e.g., World Wide Web, peer-to-peer file-sharing systems, video-on-demand systems). In this paper, we focus on analyzing the channel popularity in the context of Internet Protocol Television (IPTV). In particular, we aim at capturing two important aspects of channel popularity – the distribution and temporal dynamics of the channel popularity. We conduct in-depth analysis on channel popularity on a large collection of user channel access data from a nation-wide commercial IPTV network. Based on the findings in our analysis, we choose a stochastic model that finds good matches in all attributes of interest with respect to the channel popularity. Furthermore, we propose a method to identify subsets of user population with inherently different channel interest. By tracking the change of population mixtures among different user classes, we extend our model to a multi-class population model, which enables us to capture the moderate diurnal popularity patterns exhibited in some channels. We also validate our channel popularity model using real user channel access data from commercial IPTV network.