Although peer-to-peer (P2P) streaming can efficiently deliver live video content to large user populations, existing applications often suffer from limited video quality, periodic hiccups, and high delays. To overcome some of the limitations of today's unstructured (mesh-based) designs, we have developed and deployed FastMesh-SIM, a novel P2P streaming system that leverages proxies, push-mechanism and IP multicast to achieve lower playback delay and better stream continuity. Having control over a real P2P streaming system also gives us a rare opportunity to conduct controlled experiments where we vary major design parameters (e.g., push vs. pull delivery, IP multicast support, streaming rate, and video segment size) under a range of operating conditions (e.g., dynamics of peer churn, and different network configurations), while collecting detailed, fine-granular measurements (e.g., the various components of end-to-end delay). Analysis of the measurement data, consisting of seven trials of streaming several live TV channels for more than 100 hours to 140 peers, sheds light on how design decisions and the operating environment affect important performance metrics. Our experiments show that a push-based, proxy-P2P system can achieve low delay and good video quality, though network bottlenecks on long-haul connections can sometimes cause disruptions in a global deployment. Theory-practice gaps observed from the data are also discussed. Large-scale, global experiments are now being carried out.

We consider media streaming using application-level multicast (ALM) where packet loss has to be recovered via retransmission in a timely manner. Since packets may be lost due to congestion, node failures, and join and leave dynamics, traditional "vertical" recovery approach where upstream nodes retransmit the lost packets is no longer effective. We therefore propose lateral error recovery (LER). In LER, hosts are divided into a number of planes, each of which forms an independent ALM tree. Since error correlation across planes is low, a node effectively recovers its error by "laterally" requesting retransmission from nearby nodes in other planes. We present analysis on the complexity and recovery delay on LER. Using Internet-like topologies, we show via simulations that LER is an effective error recovery mechanism. It achieves low overhead in terms of delivery delay (i.e., relative delay penalty) and physical link stress. As compared with traditional recovery schemes, LER attains much lower residual loss rate (i.e., loss rate after retransmission) under a certain deadline constraint. The performance can be substantially improved in the presence of some reliable proxies.

In wireless sensor networks, estimating nodal positions is important for routing efficiency and location-based services. Traditional techniques based on precise measurements are often expensive and power-inefficient, while approaches based on landmarks often require bandwidth-inefficient flooding and hence are not scalable for large networks. In this paper, we propose and investigate a cost-effective and distributed algorithm to accurately estimate nodal positions for wireless sensor networks. In our algorithm, a node only needs to identify and exchange information with a certain number of neighbors (around 30) in its proximity in order to estimate its relative nodal position accurately. For location-identification, only a small number of nodes (around 10) are needed to have additional GPS capabilities to accurately estimate the absolute position of every node in the network. Our algorithm is shown to have fast convergence with low estimation error, even for large networks.

Island multicast (IM) has been recently proposed to achieve efficient global multicast, where IP multicast is used within multicast-capable domains (the so-called islands) while overlay connections are used to bridge islands. In the previously proposed scheme, the number of ping measurement to find good bridge-nodes is at least proportional to island size, and a leader needs to keep track of all its members in the island. In this paper, we improve the system scalability by presenting a bridge-node selection algorithm where both the numbers of ping measurements and members to keep track of are greatly reduced to some constants. We further propose a recovery scheme for packets lost across islands. Our scheme uses a number of recovery meshes formed by overlays of some randomly chosen nodes. Simulation results show that our bridge-node selection is efficient in terms of control overhead and achieves scalability with little cost in network stress and delay. As compared to traditional source and parent recoveries, our loss recovery scheme substantially reduces both the recovery delay and bandwidth overhead to achieve reliability.

We consider the delivery of reliable and streaming services using application-level multicast (ALM) by means of UDP, where packet loss has to be recovered via retransmission in a timely manner in order to offer high level of service. Since packets may be lost due to congestion, tree-reconfiguration or node failure, the traditional "vertical" recovery, whereby upstream nodes retransmit the lost packet is no longer effective. We therefore propose and investigate lateral error recovery (LER). In LER, hosts are divided into a number of planes, each of which forms an independent ALM tree. Since the correlation of error among the planes is likely to be low, a node can effectively recover its error "laterally" from nearby nodes in other planes. We employ the technique of global network positioning (GNP) to map the hosts into a coordinate space and identify a set of close neighbors for error recovery by constructing a Voronoi diagram for each plane. We present centralized and distributed algorithm on how to construct the Voronoi diagrams. Using Internet-like topologies, we show via simulations that our system achieves low overheads in terms of relative delay penalty and physical link stress. For reliable service, lateral recovery greatly reduces the average recovery time as compared with vertical recovery schemes. For streaming applications, LER achieves much lower residual loss rate under a certain deadline constraint.