A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing In the Cloud

Gurappa Kalyani, Amareshwari Patil

Abstract: For the reasons of high data flow of video traffics over mobile networks, the wireless link capacity fails to keep up the pace with the demand. There exists a gap between the demand and the link capacity which results in poor service quality of the video streaming over mobile networks which includes disruptions and long buffering time. Following the cloud computing technology, we suggest two solutions AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). A private agent is constructed for each mobile user in the cloud which adjusts the video bit rate using scalable video coding technique based on the return value of the wireless link condition. ESoV and AMoV create a private mediator to give video streaming services capably for every mobile client. For a particular client, AMoV lets her secret agent/mediator adaptively alter her/his streaming pour with a scalable video coding procedure depended on the response of link superiority. As well, efficient social video sharing observes the social network acquaintances among mobile clients and their personal mediators try to share video pleased in advance. We realize the AMES-Cloud framework prototype to reveal its presentation. It is shown that the confidential agents in the clouds can efficiently provide the adaptive streaming, and based on the social network learning achieve video sharing.

Keywords: Scalable Video Coding, Adaptive Video Streaming, Social Video Sharing, Cloud Computing