no / Peer-to-Peer (P2P) has become a popular live streaming delivery technology owing to its scalability and low cost. P2P streaming systems often employ multi-channels to deliver streaming to users simultaneously, which leads to a great challenge of allocating server resources among these channels appropriately. Most existing P2P systems resort to over-allocating server resources to different channels, which results in low-efficiency and high-cost. To allocate server resources to different channels efficiently, we propose a dynamic resource allocation algorithm based on a streaming quality model for P2P live streaming systems. This algorithm can improve the channel streaming quality for multi-channel P2P live streaming system and also guarantees the streaming quality of the channels under extreme Internet conditions. In an experiment, the proposed algorithm is validated by the trace data.

Peer to peer networks are becoming increasingly popular among Internet users as the downloading peers share the storage and upload bandwidth load of the system. This makes it possible for a large number of users to share a data file available at a server without the server upload bandwidth becoming a bottleneck. The P2P technology is being widely used not only for file sharing but also for video on demand, live streaming and IPTV. The delay deadlines are more stringent in live streaming and IPTV than those in file sharing as the traffic is real time. The performance perceived by a user depends upon whether the video stream is being downloaded at the streaming rate. Coolstreaming is the first large scale P2P IPTV system. We model the multi-channel Coolstreaming system via an open queueing network. The peer dynamics at a channel is modelled by a closed queueing network working at a faster rate. We compute the expected number of substreams in the overlay of New Coolstreaming which are not being received at the proper rate. The computation of the Markov chain with a very large state space is handled using the two time scale decomposition. Further we characterize the end to end delay encountered by a video stream originating from the server and received at a user of New Coolstreaming. Three factors contribute towards the delay. The first factor is the mean path length in terms of overlay hops of the partnership graph. The second factor is the mean number of routers between any two overlay peers in the network layer and the third factor is the queueing delay at a router in the Internet. The mean shortest path length in terms of overlay peers in the New Coolstreaming graph is shown to be O(logn)where nis the number of peers in the overlay. This is done by modelling the overlay by a random graph. The mean shortest path in terms of routers in the Internet’s router level topology is seen to be at most O(logNI)where NIis the number of routers in the Internet. We also discuss a method by which we can get the mean delay at a router in the Internet. Thus, the mean end to end delay in New Coolstreaming is shown to be upper bounded by O(lognlogNIE[W])where E[W]is the mean delay at a router in the Internet.

Media streaming over the Internet is becoming increasingly popular. Currently, most media is delivered using global content-delivery networks, providing a scalable and robust client-server model. However, content delivery infrastructures are expensive. One approach to reduce the cost of media delivery is to use peer-to-peer (P2P) overlay networks, where nodes share responsibility for delivering the media to one another. The main challenges in P2P media streaming using overlay networks include: (i) nodes should receive the stream with respect to certain timing constraints, (ii) the overlay should adapt to the changes in the network, e.g., varying bandwidth capacity and join/failure of nodes, (iii) nodes should be intentivized to contribute and share their resources, and (iv) nodes should be able to establish connectivity to the other nodes behind NATs. In this work, we meet these requirements by presenting P2P solutions for live media streaming, as well as proposing a distributed NAT traversal solution. First of all, we introduce a distributed market model to construct an approximately minimal height multiple-tree streaming overlay for content delivery, in gradienTv. In this system, we assume all the nodes are cooperative and execute the protocol. However, in reality, there may exist some opportunistic nodes,  free-riders, that take advantage of the system, without contributing to content distribution. To overcome this problem, we extend our market model in Sepidar to be effective in deterring free-riders. However, gradienTv and Sepidar are tree-based solutions, which are fragile in high churn and failure scenarios. We present a solution to this problem in GLive that provides a more robust overlay by replacing the tree structure with a mesh. We show in simulation, that the mesh-based overlay outperforms the multiple-tree overlay. Moreover, we compare the performance of all our systems with the state-of-the-art NewCoolstreaming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The Gradient overlay organizes nodes in a topology using a local utility value at each node, such that nodes are ordered in descending utility values away from a core of the highest utility nodes. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay. We use a gossip-based peer sampling service in our streaming systems to provide each node with a small list of live nodes. However, in the Internet, where a high percentage of nodes are behind NATs, existing gossiping protocols break down. To solve this problem, we present Gozar , a NAT-friendly gossip-based peer sampling service that: (i) provides uniform random samples in the presence of NATs, and (ii) enables direct connectivity to sampled nodes using a fully distributed NAT traversal service. We compare Gozar with the state-of-the-art NAT-friendly gossip-based peer sampling service, Nylon, and show that only Gozar supports one-hop NAT traversal, and its overhead is roughly half of Nylon’s.   / QC 20110517