Most of the commercial peer-to-peer (P2P) video streaming deployments support hundreds of channels and are referred to as multichannel systems. Recent research studies have proposed specific protocols to improve the streaming quality for all channels by enabling cross-channel cooperation among multiple channels. In this paper, we focus on the following fundamental problems in designing cooperating multichannel systems: 1) what are the general characteristics of existing and potential designs? and 2) under what circumstances should a particular design be used to achieve the desired streaming quality with the lowest implementation complexity? To answer the first question, we propose simple models based on linear programming and network-flow graphs for three general designs, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-aware bandwidth allocation Approach (PCA), and Active Channel-aware bandwidth allocation Approach (ACA), which provide insight into understanding the key characteristics of cross-channel resource sharing. For the second question, we first develop closed-form results for two-channel systems. Then, we use extensive numerical simulations to compare the three designs for various peer population distributions, upload bandwidth distributions, and channel structures. Our analytical and simulation results show that: 1) the NBA design can rarely achieve the desired streaming quality in general cases; 2) the PCA design can achieve the same performance as the ACA design in general cases; and 3) the ACA design should be used for special applications.

It is challenging to provide delay-bounded service in a large-scale P2P live streaming system since a P2P streaming system is not scalable from the perspective of playback delay. However, certain peers called subscribers are more sensitive to playback delay than other peers, and the violation of the delay bound dramatically affects their satisfaction. In this paper, we study subscriber bounded delay (SBD) problem, which aims to provide bounded delay service to subscribers and best-effort delay service to ordinary peers in a large-scale single channel P2P live streaming system. We formulate the SBD as a decision problem and prove that it is NP-Complete. Then we propose a decentralized heuristic called the high fanout promotion (HFP) algorithm, which helps the system to serve the maximum number of subscribers with delay-bounded service, and to provide best-effort delay service to remaining subscribers and ordinary peers. We evaluate its performance using simulation experiments and compare our approach with the naive greedy algorithm and the general delay minimization approaches in the literature. Our extensive packet-level simulations show that our distributed solution can serve more subscribers with bounded delay video service compared to the other two methods (17%-50% in our simulations). Our distributed algorithm works well in both homogeneous and heterogeneous environments, and it converges very fast.

Nowadays, millions of Internet users watch and upload a large number of videos on User Generated Content (UGC) sites (e.g., Youtube) everyday. Moreover, online videos about hot events, such as breaking news and Olympic games, attract lots of users. In this paper, we study the characteristics of hot-event videos by collecting video traces of the largest UGC site in China for 28 days. We first empirically study statistical properties of such videos and find that hot-event videos contribute a large number of views, even though the total number of hot-event videos is relatively small. In addition, there exist extremely active uploaders and top 10% active uploaders upload over 60% videos. The video popularity demonstrates high skewness, where top 5% the most popular videos contribute over 80% views. Finally, we analyze the popularity evolution of hot-event videos using the consecutive 28-day video traces. The popularity of the studied videos decays very fast and most of these videos remain popular for only a week. Our findings reflect the most recent developments of UGC sites, which provide technical and commercial insights for engineers and UGC site owners.

Most of the commercial P2P video streaming deployments support hundreds of channels and are referred to as multichannel systems. Measurement studies show that bandwidth resources of different channels are highly unbalanced and thus recent research studies have proposed various protocols to improve the streaming qualities for all channels by enabling cross-channel cooperation among multiple channels. However, there is no general framework for comparing existing and potential designs for multi-channel P2P systems. The goal of this paper is to establish tractable models for answering the fundamental question in multi-channel system designs: Under what circumstances, should a particular design be used to achieve the desired streaming quality with the lowest implementation complexity? To achieve this goal, we first classify existing and potential designs into three categories, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-aware bandwidth allocation Approach (PCA) and Active Channel-aware bandwidth allocation Approach (ACA). Then, we define the bandwidth satisfaction ratio as a performance metric to develop linear programming models for the three designs. The proposed models are independent of implementations and can be efficiently solved due to the linear property, which provides a way of numerically exploring the design space of multi-channel systems and developing closed-form solutions for special systems.

The success of commercial Peer-to-Peer (P2P) video streaming systems has triggered interest in exploiting end users' bandwidth to reduce the operating costs of IPTV and Content Distribution Networks (CDN) and to improve the user-perceived service quality. Traditionally, users watching different channels are organized into separate overlays, where there is no cooperation among different channels. However, based on measurement studies, cross-channel cooperation is found to be desirable due to the bandwidth imbalance among different channels. In this paper, we focus on studying the characteristics of existing and potential designs to help system designers choose proper cross-channel cooperation strategies considering efficiency and implementation complexity. Specifically, we propose simple models based on network flow graphs for three general designs, namely Naive Bandwidth allocation Approach (NBA), Passive Channel-aware bandwidth allocation Approach (PCA) and Active Channel-aware bandwidth allocation Approach (ACA) respectively, which capture the key characteristics of different designs. We develop closed-form results for two-channel systems. Then, we use extensive numerical simulations to compare the three designs for various peer population distributions, upload bandwidth distributions and channel structures. Our analytical and simulation results show that: 1) Though the NBA design can be implemented with low complexity, it cannot efficiently use user's bandwidth in general cases; 2) the PCA design can achieve the same bandwidth utilization efficiency as the ACA design in general cases; and 3) the ACA design should be used for special applications and systems in which a user is restricted to watch only one channel at a time.

Most of the literature on peer-to-peer (P2P) live streaming focuses on how to provide best-effort streaming quality by efficiently using the system bandwidth; however, there is no guarantee about the provided streaming quality. This paper considers how to provide statistically guaranteed streaming quality to a P2P live streaming system. We study a class of admission control algorithms which statistically guarantee that a P2P live streaming system has sufficient overall bandwidth. Our results show that there is a tradeoff between the user blocking rate and user-behavior insensitivity (i.e., whether the system performance is insensitive to the fine statistics of user behaviors). We also find that the system performance is more sensitive to the distribution change of user inter-arrival times than to that of user lifetimes.

Multi-view peer-to-peer (P2P) live streaming systems have recently emerged, where a user can simultaneously watch multiple channels. Previous work on multi-view P2P streaming solves the fundamental inter-channel bandwidth competition problem at the individual peer level, and thus can be used with very limited types of streaming protocols. In this paper, we propose a new protocol for multi-view P2P streaming, called divide-and-conquer (DAC), which efficiently solves the inter-channel bandwidth competition problem using a divide-and conquer strategy at the channel level, and thus is flexible to work with various streaming protocols. This makes DAC more suitable for upgrading current single-view P2P live streaming systems to multi-view P2P live streaming systems. Our extensive packetlevel simulations show that DAC is efficient in allocating the overall system bandwidth among competing channels, is flexible in working with various streaming protocols, and is scalable in supporting a large number of users and channels.

Data-driven (or swarming based) streaming is one of the popular ways to distribute live multimedia streaming traffic over peer-to-peer (P2P) networks. The efficiency and user satisfaction highly depend on the constructed overlays. The common neighbor selection algorithms in existing overlay construction schemes usually randomly select a fixed number of neighbors which satisfy the selection requirements, such as end-to-end delay or a peerpsilas sojourn time. However, this fixed random neighbor-selection algorithm (FRNS) neglects the peerspsila upload bandwidth heterogeneity and therefore, the upload bandwidth cannot be efficiently used. In this paper, we propose a variable random neighbor-selection (VRNS) scheme to alleviate the problems due to bandwidth heterogeneity, and in which the number of neighbors with different upload bandwidths is dynamically determined by the statistical bandwidth information of the system. Our proposed scheme is shown to outperform FRNS based upon a large volume of carefully designed simulations.

Motivated by the success of the Picture in Picture feature of the traditional TV, several commercial Peer-to-Peer MultiMedia Streaming (P2PMMS) applications now support the multi-view feature, with which a user can simultaneously watch multiple channels on its screen. This paper considers the peer selection problem in multi-view P2PMMS. This problem has been well studied in the traditional single-view P2PMMS; however, it becomes more complicated in multi-view P2PMMS, mainly due to the fact that a peer watching multiple channels joins multiple corresponding overlays. In this paper, we propose a novel peer selection algorithm, called Channel-Aware Peer Selection (CAPS), where a peer selects its neighboring peers based on the channel subscription of the system, in order to efficiently utilize the bandwidth of all peers in the system, especially those peers watching multiple channels. The results of a large-scale simulation with 10,000 peers and 4 channels shows that CAPS can significantly improve the system performance over the straightforward Random Peer Selection (RPS), which is widely used in single-view P2PMMS networks.