A Skelton of Efficient Public Video Sharing and Adaptive Mobile Video Streaming in Cloud Computing
D. Sravani , B. Vijaya Bhaskar Reddy.
Based on demands of overloading of video traffic over mobile networks which are been sourcing for the wireless network capacity which can be keep up with respect to the existing demand. We are facing long buffering time and intermittent disruptions. With this perspective of mind we are developing a new concept through cloud computing technology; Here by we are proposing a new mobile video streaming framework named dubbed AMES-Cloud, This skelton consists two main parts. They are named as follows AMoV (adaptive mobile video streaming) and ESoV (efficient public video sharing). Both framework parts build a private agent to provide video streaming services efficiently for every mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and also their private agents try to deploy video content in advance. Here we are going to implement a prototype of the AMES-Cloud framework to demonstrate its performance, which shows that the private agents in the clouds can effectively provide the adaptive streaming and perform video sharing based on the social network analysis?
Computing, Mobile Cloud Computing, Adaptive video streaming, , scalable video coding, Public video sharing
 Xiaofei Wang, , MinChen,, Ted Taekyoung Kwon, LaurenceT. Yang, Victor C. M. Leung, “Video Streaming and Efficient Social Video Sharing in the Clouds” IEEE Transactions on multimedia, vol. 15, no. 4, june 2013
 “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011–2016,” CISCO, 2012.
 Y. Li, Y. Zhang, and R. Yuan, “Measurement and analysis of a large scale commercial mobile Internet TV system,” in Proc. ACM Internet Meas. conf., 2011, pp. 209–224.
 T. Taleb and K. Hashimoto, “MS2: A novel multi-source mobilestreaming architecture,” IEEE Trans. Broadcasting, vol. 57, no.3, pp. 662–673, Sep. 2011.
 X. Wang, S. Kim, T. Kwon, H. Kim, and Y. Choi, “Unveiling the bittorrent performance in mobile WiMAX networks,” in Proc. Passive Active Meas. Conf., 2011, pp. 184–193.
 A. Nafaa, T. Taleb, and L. Murphy, “Forward error correction Adaptation strategies for media streaming over wireless networks,” IEEE Commun. Mag., vol. 46, no. 1, pp. 72–79, Jan. 2008
 IBSG Cisco, “Mobile Consumers reach for the Cloud”
 Peter Mell, Tim Grance, “The NIST definition of Cloud Computing”, v15.
.Han Qi, Abdullah Gani, “Research on Mobile Cloud computing: Review,Trend and Perspectives” in Proceedings of the Second International Conference on Digital information and Communication Technology and its Applications (DICTAP), IEEE, Pages 195-202, 2012
 Xiaopeng Fan, Jiannong Cao, and Haixia Mao, “A Survey on Mobile Cloud Computing”, ZTE Corporation
 Sean Marston, Zhi Li, Subhajyoti Bandyopadhyay, Juheng Zhang, Anand Ghalsasi, “Cloud Computing – The business perspective”, Decision Support System, Volume 51, Issue 1, Pages 176-189, 2011
 Hoang T. Dinh, Chonho Lee, Dusit Niyato, and Ping Wang, “A survey of Mobile Cloud Computing: Architecture, Applications and Approaches”, Wireless Communications and Mobile Computing, 2011
 I. Foster, Y. Zhao, I. Raicu, and S. Lu, “Cloud Computing and Grid Computing 360-Degree Compared,” in Proceedings of Workshop on Grid Computing Environments (GCE), Pages 1- 10, 2009
 L. Youseff, M. Butrico, and D. Da Silva, “Toward a unified ontology of cloud computing,” in Grid Computing Environments Workshop, IEEE, Pages 1-10, 2008
[D. Sravani, B. Vijaya Bhaskar Reddy. (2014), A Skelton of Efficient Public Video Sharing and Adaptive Mobile Video Streaming in Cloud Computing, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-2, Issue-5, Page No-60-64], (ISSN 2347 - 5552). www.ijircst.org
Department of CSE , Shri Shirdi Sai Institute of Science and Engineering, Anantapur, A.P , India