7,633 research outputs found

    Channel-Aware Peer Selection in Multi-View Peer-to-Peer Multimedia Streaming

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    Motivated by the success of the Picture in Picture feature of the traditional TV, several commercial Peer-to-Peer Multi-Media 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

    Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

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    In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers

    Multi-View Video Packet Scheduling

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    In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time

    Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach

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    Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows to leverage the cooperation among peers, guaranteeing to serve every video request with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms, and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to warrant its users a very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, both in terms of robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.Comment: 12 pages, 17 figures, this work has been submitted to the IEEE journal on selected Area in Communication

    Efficient and flexible inter-overlay scheduling of media streams for multi-channel P2P streaming

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    Existing studies on channel bandwidth imbalance in P2P multi-channel streaming systems have been exclusively focused on inter-overlay bandwidth allocation. However, an efficient inter-overlay scheduling algorithm is still in lack for benefactors. To this end, this paper presents an inter-overlay substream scheduling algorithm compatible with various overlay meshes for active inter-overlay cooperation, through which the outbound bandwidth of benefactors can be efficiently utilized and bandwidth-deprived channels receiving benefactions can attain a better streaming quality. © 2012 IEEE.published_or_final_versionThe 2012 International Conference on Computing, Networking, and Communications (ICNC 2012), Maui, HI., 30 January-2 February 2012. In Proceedings of ICNC, 2012, p. 820-82

    Network Awareness of P2P Live Streaming Applications

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    Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work. In this paper we present large scale sets of experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts. Our goal is to assess what level of "network awareness" has been embedded in the applications, i.e., what parameters mainly drive the peer selection and data exchange. By using a general framework that can be extended to other systems and metrics, we show that all applications largely base their choices on the peer bandwidth, i.e., they prefer high-bandwidth users, which is rather intuitive. Moreover, TVAnts and PPLive exhibits also a preference to exchange data among peers in the same autonomous system the peer belongs to. However, no evidence about preference versus peers in the same subnet or that are closer to the considered peer emerges. We believe that next-generation P2P live streaming applications definitively need to improve the level of network-awareness, so to better localize the traffic in the network and thus increase their network-friendliness as wel
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