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.

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.

Abstract—Since the inception of network coding in information theory, we have witnessed a sharp increase of research interest in its applications in communications and networking, where the focus has been on more practical aspects. However, thus far, network coding has not been deployed in real-world commercial systems in operation at a large scale, and in a production setting. In this paper, we present the objectives, rationale, and design in the first production deployment of random network coding, where it has been used in the past year as the cornerstone of a large-scale production on-demand streaming system, operated by UUSee Inc., delivering thousands of on-demand video channels to millions of unique visitors each month. To achieve a thorough understanding of the performance of network coding, we have collected 200 Gigabytes worth of real-world traces throughout the 17-day Summer Olympic Games in August 2008, and present our lessons learned after an in-depth trace-driven analysis. I.

PPLive is a popular P2P video system used daily by millions of people worldwide. Achieving this level of scalability depends on users making contributions to the system, but currently, these contributions are neither verified nor rewarded. In this paper, we describe the design and implementation of Contracts, a new, practical approach to providing contribution incentives in P2P live streaming systems. Using measurements of tens of thousands of PPLive users, we show that widely-used bilateral incentive strategies cannot be effectively applied to the live streaming environment. Contracts adopts a different approach: rewarding globally effective contribution with improved robustness. Using a modified PPLive client, we show that Contracts both improves performance and strengthens contribution incentives. For example, in our experiments, the fraction of PPLive clients using Contracts experiencing loss-free playback is more than 4 times that of native PPLive. 1

The 2008 Beijing Olympics was an interesting event from a VoD perspective because it involved near real-time video delivery at massive scales over multiple days of a high-profile event. We present some measurement-driven insights into this event through a unique dataset obtained from ChinaCache, the largest CDN in China. The dataset is unique in three respects. First, it gives a “white-box ” view into user access patterns which would otherwise be impossible. Second, since the CDN serves different content providers, it allows to compare and contrast the effects of different presentation models on end users. Third, the nature of the content itself is vastly different from traditional VoD systems in terms of the real-time and event-driven nature, which gives rise to unique effects. The dataset allows us to investigate a wide range of interesting issues: (1) how the live nature of the events causes differences in access patterns compared to traditional VoD and User-Generated Content (UGC) systems, (2) how the presentation models affect user behavior, and (3) flash-crowd phenomena. Based on these observations, we discuss implications for future live VoD systems.

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Abstract—Traditional Video-on-Demand (VoD) systems reply purely on servers to stream video content to clients, which does not scale. In recent years, Peer-to-peer assisted VoD (P2P VoD) has proven to be practical and effective [1]. In P2P VoD, each peer contributes some storage to store videos (or segments of videos) to help the video server. Assuming peers have sufficient bandwidth for the given video playback rate, a fundamental question is what is the relationship between the storage capacity (at each peer), the number of videos, the number of peers and the resultant off-loading of video server bandwidth. In this paper, we use a simple statistical model to derive this relationship. We propose and analyze a generic replication algorithm RLB which balances the service to all movies, for both deterministic and random demand models, and both homogeneous and heterogeneous peers (in upload bandwidth). We use simulation to validate our results, for sensitivity analysis and for comparisons with other popular replication algorithms. This study leads to several fundamental insights for design P2P VoD systems in practice. I.

We present our design and deployment experiences with LiveSky, a commercially deployed hybrid CDN-P2P live streaming system. CDNs and P2P systems are the common techniques used for live streaming, each having its own set of advantages and disadvantages. LiveSky inherits the best of both worlds: the quality control and reliability of a CDN and the inherent scalability of a P2P system. We address several key challenges in the system design and implementation including (a) dynamic resource scaling while guaranteeing stream quality, (b) providing low startup latency, (c) ease of integration with existing CDN infrastructure, and (d) ensuring network-friendliness and upload fairness in the P2P operation. LiveSky has been commercially deployed and used for several large-scale live streaming events serving more than ten million users in China. We evaluate the performance of LiveSky using data from these real-world deployments. Our results indicate that such a hybrid CDN-P2P system provides quality and user performance comparable to a CDN and effectively scales the system capacity when the user volume exceeds the CDN capacity.

With the ever-increasing P2P Internet traffic, recently much attention has been paid to the topology mismatch between the P2P overlay and the underlying network due to the large amount of cross-ISP traffic. Mainly focusing on BitTorrent-like file sharing systems, several recent studies have demonstrated how to efficiently bridge the overlay and the underlying network by leveraging the existing infrastructure, such as CDN services or developing new application-ISP interfaces, such as P4P. However, so far the traffic locality in existing P2P live streaming systems has not been well studied. In this work, taking PPLive as an example, we examine traffic locality in Internet P2P streaming systems. Our measurement results on both popular and unpopular channels from various locations show that current PPLive traffic is highly localized at the ISP level. In particular, we find: (1) a PPLive peer mainly obtains peer lists referred by its connected neighbors (rather than tracker servers) and up to 90 % of listed peers are from the same ISP as the requesting peer; (2) the major portion of the streaming traffic received by a requesting peer (up to 88 % in popular channels) is served by peers in the same ISP as the requestor; (3) the top 10 % of the connected peers provide most (about 70%) of the requested streaming data and these top peers have smaller RTT to the requesting peer. Our study reveals that without using any topology information or demanding any infrastructure support, PPLive achieves such high ISP level traffic locality spontaneously with its decentralized, latency based, neighbor referral peer selection strategy. These findings provide some new insights for better understanding and optimizing the network- and user-level performance in practical P2P live streaming systems. 1.

Although there are several successful commercial deployments of live P2P streaming systems, the current designs (i) lack incentives for users to contribute bandwidth resources, (ii) lack adaptation to aggregate bandwidth availability, and (iii) exhibit poor video quality when bandwidth availability falls below bandwidth supply. In this paper, we propose, prototype, deploy and validate LayerP2P, a P2P live streaming system that addresses all three of these problems. LayerP2P combines layered video, mesh P2P distribution, and a tit-for-tat-like algorithm, in a manner such that a peer contributing more upload bandwidth receives more layers and consequently better video quality. We implement LayerP2P (including seeds, clients, trackers, and layered codecs), deploy the prototype in PlanetLab, and perform extensive experiments. We also examine a wide range of scenarios using trace-driven simulations. The results show that LayerP2P has high efficiency, provides differentiated service, adapts to bandwidth deficient scenarios, and provides protection against free-riders.