User Generated Content (UGC) video applications, such as YouTube, are enormously popular. UGC systems can potentially reduce their distribution costs by allowing peers to store and redistribute the videos that they have seen in the past. We study peer-assisted UGC from three perspectives. First, we undertake a measurement study of the peer-assisted distribution system of Tudou (a popular UGC network in China), revealing several fundamental characteristics that models need to take into account. Second, we develop analytical models for peer-assisted distribution of UGC. Our models capture essential aspects of peer-assisted UGC systems, including system size, peer bandwidth heterogeneity, limited peer storage, and video characteristics. We apply these models to numerically study YouTube-like UGC services. And third, we develop analytical models to understand the rate at which users would install P2P client applications to make peer-assisted UGC a success. Our results provide a comprehensive study of peer-assisted UGC distribution, exposing its fundamental characteristics and limitations.

YouTube uploaders are the central agents in the YouTube phenomenon. We conduct extensive measurement and analysis of YouTube uploaders. We estimate YouTube scale and examine the uploading behavior of YouTube users. We demonstrate the positive reinforcement between on-line social behavior and uploading behavior. Furthermore, we examine whether YouTube users are truly broadcasting themselves, via characterizing and classifying videos as either user generated or user copied.

Incentive mechanisms play a critical role in P2P systems. Private BitTorrent sites use a novel incentive paradigm, where the sites record upload and download amounts of users and require each user to maintain its upload-to-download ratio above a specified threshold. This paper explores in-depth incentives in private P2P file-sharing systems. Our contributions are threefold. We first conduct a measurement study on a representative private BitTorrent site, examining how incentives influence user behavior. Our measurement study shows that, as compared with public torrents, a private BitTorrent site provides more incentive for users to contribute and seed. Second, we develop a game theoretic model and analytically show that the ratio mechanism indeed provides effective incentives. But existing ratio incentives in private BitTorrent sites are vulnerable to collusions. Third, to prevent collusion, we propose an upload entropy scheme, and show through analysis and experiment that the entropy scheme successfully limits colluding, while rarely affecting normal users who do not collude.

The success of future P2P applications ultimately depends on whether users will contribute their bandwidth, CPU and storage resources to a larger community. In this paper, we propose a new incentive paradigm, Networked Asynchronous Bilateral Trading (NABT), which can be applied to a broad range of P2P applications. In NABT, peers belong to an underlying social network, and each pair of friends keeps track of a credit balance between them. When user Alice provides a service (a file, storage space, computation and so on) to her friend Bob, she charges Bob credits. Thus, in NABT, there is no global currency; instead, there are only credit balances maintained between pairs of friends. NABT allows peers to supply each other asynchronously and further allows peers to trade with remote peers through intermediaries. We theoretically show that NABT is perfectly efficient with balanced demands and supports "networked tit-for-tat". The efficiency of NABT with unbalanced demands is determined by the min-cut of credit limits of the underlying social network. Using simulations driven by MySpace traces, we demonstrate that a simple two-hop NABT design can have high trading efficiency, provide service differentiation, exploit trading intermediaries, and discourage free-riders.

A private BitTorrent site (also known as a "Bit Torrent darknet") is a collection of torrents that can only be accessed by members of the darknet community. The private BitTorrent sites also have incentive policies which encourage users to continue to seed files after completing downloading. Although there are at least 800 independent BitTorrent darknets in the Internet, they have received little attention in the research community to date. We examine BitTorrent darknets from macroscopic, medium-scopic and microscopic perspectives. For the macroscopic analysis, we consider 800+ private sites to obtain a broad picture of the darknet landscape, and obtain a rough estimate of the total number of files, accounts, and simultaneous peers within the entire darknet landscape. Although the size of each private site is relatively small, we find the aggregate size of the darknet landscape to be surprisingly large. For the medium-scopic analysis, we investigate content overlap between four private sites and the public BitTorrent ecosystem. For the microscopic analysis, we explore in-depth one private site and examine its user behavior. We observe that the seed-to-leecher ratios and upload-to-download ratios are much higher than in the public ecosystem. The macroscopic, medium-scopic and microscopic analyses when combined provide a vivid picture of the darknet landscape, and provide insight into how the darknet landscape differs from the public BitTorrent ecosystem.

Wireless video multicast efficiently streams video to multiple receivers. When designing a wireless video multicast system, the system must be able to handle (i) packet losses induced by the underlying wireless channel and (ii) receiver heterogeneity in channel condition in a multicast group. We propose Layer Bargaining, a wireless video multicast design in infrastructure-based wireless networks that simultaneously addresses both of the above problems. To combat packet losses and improve received video quality, we propose a layered hybrid ARQ scheme that provides unequal protection to layered video by exploring light-weight feedback. To deal with receiver heterogeneity, we propose a framework based on Nash bargaining game for operating point selection in a multicast group. Our simulation results show that the layered hybrid ARQ scheme significantly outperforms the conventional hybrid ARQ scheme with single layer video and the layered FEC scheme. The results also show that the game-based operating point selection provides a high overall system performance while facilitating fairness among receivers. We carefully examine the overhead and the computational complexity of our proposed schemes, through both theoretical analysis and OPNET simulation/experiment, and show that Layer Bargaining is practically feasible under representative settings.

Although there are several successful commercial deployments of live P2P streaming systems, the current designs 1) lack incentives for users to contribute bandwidth resources, 2) lack adaptation to aggregate bandwidth availability, and 3) exhibit poor video quality when bandwidth availability falls below bandwidth supply. In this paper, we propose, prototype, deploy, and validateLayerP2P, 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.

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, layered 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 free-riders.

In this paper, we consider applying multiple description coding in mesh-pull P2P live streaming networks to provide incentives for redistribution. In our system, a video is encoded into multiple descriptions with each description having equal importance. We consider a heterogeneous system with peers having different uplink bandwidths. We design a distributed protocol in which a peer contributing more uplink bandwidth receives more descriptions and consequently better video quality. Previous approaches consider single-layer video, where each peer receives the same video quality no matter how much bandwidth it contributes to the system. The simulation results show that our approach can provide differentiated video quality commensurate with a peer's contribution to other peers.

This paper describes cross-layer optimization strategies and simulation results for H.264 videomulticast over wireless LAN. The proposed scheme takes into account the varying channe conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding. In particular, source coding parameters (intra update and quantization) and application-layer FEC code rate are chosen jointly to optimize a multicast performance criterion, based on feedbacks from all multicast receivers. Two performance criteria for video multicast are investigated and compared.

In this paper, we examine the prefetching strategies in a peer-driven video on-demand system. In our design, each video is encoded into multiple low bit-rate substreams and copies of the substreams are distributed to the participating peers. When a peer streams in a substream of rate r, it instead streams at rate rcirc, where r>rcirc. In this manner, if one of the peer's suppliers disconnects, the client peer can tap the reservoir of prefetched bits while searching for a replacement server, thereby avoiding any glitches or reduced visual quality. We examine how to assign prefetching rates to each of substreams as a function of their importance. Our studies show that appropriate prefetching strategies can bring significant performance improvements for both multiple description and layered videos

Peer-to-peer video streaming has emerged as an important means to transport stored video. The peers are less costly and more scalable than an infrastructure-based video streaming network which deploys a dedicated set of servers to store and distribute videos to clients. In this paper, we investigate streaming layered encoded video using peers. Each video is encoded into hierarchical layers which are stored on different peers. The system serves a client request by streaming multiple layers of the requested video from separate peers. The system provides unequal error protection for different layers by varying the number of copies stored for each layer according to its importance. We evaluate the performance of our proposed system with different copy number allocation schemes through extensive simulations. Finally, we compare the performance of layered coding with multiple description coding.