The exponential increasing traffic demands of mobile streaming services over a network have been unpleasant the wireless link capability cannot keep up with the growing traffic load. There is some space between link capability and traffic demand with time varying link condition which gives results in poor quality of streaming services such as constant interruption and long buffering delays. The AMES-Cloud proposes a new video streaming methods which is User-Adaptive Mobile Video Streaming (AMoV) and User-Behavior Oriented Video Pre-fetching (UBoP). The AMoV and UBoP create a private mediator for efficient video streaming distribution process. The private mediator adjust the streaming flow and reduces the traffic using scalable video coding technique ( SVC ) which shows the social interaction among each user. The video quality of streaming is based on the feedback of link quality. This shows the effectively streaming and sharing service over a network. The efficient background pre-fetching is also done which is based on user resolution and bandwidth. It provides advance and excellence service of video streaming while using the networking and computing assets resourcefully.

1. 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.
2. SaurabhGoel "Cloud-Based Mobile Video Streaming Techniques" International Journal of Wireless & Mobile Networks (IJWMN) Vol. 5, No. 1, February 2013.
3. 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.
4. Y. Li, Y. Zhang, and R. Yuan, "AMVS-NDN: Adaptive Mobile Video Streaming and Sharing in Wireless Named Data Networking ," in Proc. ACM Internet Meas. conf. , 2011, pp. 209–224.
5. Juan Carlos Fernandez, TarikTaleb, Mohsen Guizani, Nei Kato, " Bandwidth Aggregation-Aware Dynamic QoS Negotiation for Real-Time Video Streaming in Next-Generation Wireless Networks" IEEE Trans. multimedia, vol. 11, no. 6, pp. 1082–1093, October. 2009.
6. T. Taleb, K. Kashibuchi, A. Leonardi, S. Palazzo, K. Hashimoto, N. Kato, and Y. Nemoto, "A cross-layer approach for an efficient delivery of TCP/RTP-based multimedia applications in heterogeneous wireless networks," IEEE Trans. Veh. Technol. , vol. 57, no. 6, pp. 3801–3814,Jun. 2008
7. A. Naffa,T. 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.
8. K. Zhang, J. Kong, M. Qiu, and G. L. Song, "Multimedia layout adaptation through grammatical lspecifications,"ACM/Springer Multimedia Syst. , vol. 10, no. 3, pp. 245–260, 2005
9. G. Wang and T. E. Ng, "The impact of virtualization on network performance of amazon EC2 data center," in Proc, IEEE INFOCOM, 2010, pp. 1163-1171.
10. H. Schwarz and M. Wien, "The Scalable video coding extension of the H. 264/AVC standard," IEEE Signal Process. Mag. , vol. 25, no. 2, pp,135-141, Feb, 2008.
11. N. Davies, "The Case for VM-Based Cloudlets in Mobile Computing," in IEEE Pervasive Computing, vol. 8, no. 4, pp. 14–23, 2009.
12. P. McDonagh, C. Vallati, A. Pande, and P. Mohapatra, "Quality-oriented scalable video delivery using H. 264 SVC on an LTE network," in Proc. WPMC, 2011.
13. Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: State-oftheart and research challenges," J. Internet Services Applic. , vol. 1, no. 1, pp. 7–18, Apr. 2010.
14. D. Niu, H. Xu, B. Li, and S. Zhao, "Quality-assured cloud bandwidth auto-scaling for video-on-Demand applications," in Proc. IEEE INFOCOM,2012, pp. 460–468.
15. Y. G. Wen, W. W. Zhang, K. Guan, D. Kilper, and H. Y. Luo, "Energyoptimal execution policy for a Cloudassisted mobile application platform," Sep. 2011.
16. J. M. Kang, S. S. Seo, and J. W. Hong, "Personalized battery lifetime pprediction for mobile devices based on usage patterns," J. Computing Sci. Eng. , vol. 5, no. 4, pp. 338-345, 2011.
17. S. Chetan, G. Kumar, K. Dinesh, K. Mathew, and M. A. Abhimanyu, "Cloud Computing for Mobile World,"2010.
18. V. Singh and I. D. D. Curcio, "Rate adaptation for conversational 3G video," in Proc. IEEE INFOCOM Workshop, 2009, pp. 205-211.
19. Y. Chen, L. Qiu, W. Chen, L. Nguyen, and R. Katz, "Clustering web content for efficient replication," in Proc. IEEE ICNP, 2002, pp. 165-174.
20. K. Tappayuthpijarn, G. Liebl, T. Stockhammer, and E. Steinbach, "Adaptive video streaming over a mobile network with TCP friendly rate control," in Proc. IWCMC, 2009, pp. 1325–1329.
21. Y. Chen, L. Qiu, W. Chen, L. Nguyen, and R. Katz, "Clustering web content for efficient replication," in Proc. IEEE ICNP, 2002, pp. 165–174.
22. M. Cha, H. Kwak, P. Rodriguez, Y. Y. Ahn, and S. Moon, "I tube, you tube, everybody tubes: Analyzing the world's largest user generated content video system," in Proc. ACM Internet Meas. Conf. , 2007, pp. 1–14.
23. A. Zambelli, "IIS smooth streaming technical overview," Microsoft Corp. , 2009.
24. Y. Zhang, W. Gao, G. Cao, T. L. Porta, B. Krishnamachari, and A. Iyengar, "Social-Aware Data Diffusion in Delay Tolerant MANET," Handbook ofOptimization in Complex Networks: Communication and Social Networks, 2010.
25. Z. Wang, L. Sun, C. Wu, and S. Yang, "Guiding Internet- Scale VIdeo Service Deployment Using Microblog-Based prediction. "In IEEE INFOCOM. 2012.

Adaptive Video Streaming Scalable Video Coding (svc) Social Video Sharing Video Cloud And Video Base.