Piece hunting algorithm for video content management

The circulation of video content over P2P frameworks has turned into a prominent and practical-alternative as of late because of the expanding online accessibility of the content. Furthermore, the differing qualities of end-client terminals utilized for expending content requests the provisioning of content in diverse qualities. A piece hunting algorithm for video content needs to guarantee that the pieces are accepted in time and additionally that the best conceivable quality that might be processed by the end-client terminal and also downloaded in a specific networking conditions is given. In this paper, we portray our algorithm for piece hunting which is focused around probabilistic distribution. We utilize an idea of self-assertive stroller & develop an algorithm for the same under the name of arbitrary stroller. For the process of searching, we contend that arbitrary strolls accomplish very high improvement over the process of flooding on account of reissuing the same request in a periodic manner. The key specialized element of our methodology is a profound aftereffect of stochastic courses of action demonstrating that examples taken from sequential steps of an arbitrary stroll can attain quite same results as that of independent sampling.