Application Level Multicast Tree Research Articles

The Application Level Multicast Technique Algorithms Oriented to P2P Video

Firstly challenges in P2P media streaming applications were pointed out, then some recent research results such as application-level multicast tree, heterogeneity of network and incentive mechanism and so on were introduced. This paper has proposed a new model of application level multicast named DHCM (density-based hierarchical clustering multicast)which has improved IHC arithmetic．DHCM divides the hosts into many hierarchies according to their density，and constructs a density tree to realize the shortest routing．The tree delivers the content of video server to each host in density tree and uses a P2P scheme in data transmission．By this way the application-level multicast has been realized．This density tree has the homogeneity and monotonic properties．The experiment result has proved that DHCM can transmit the video stream efficiently and robustly.

Lateral error recovery for media streaming in application-level multicast

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.