Home > 2 ABSTRACT While demands on video traffic over mobile networks have been souring, the wireless link capacity canno

2 ABSTRACT While demands on video traffic over mobile networks have been souring, the wireless link capacity canno


 

                                                ABSTRACT 
 

       While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each 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 their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis. 
 
 

    EXISTING SYSTEM

     Cloud computing promises lower costs, rapid scaling, easier maintenance, and service availability anywhere, anytime, a key challenge is how to ensure and build confidence that the cloud can handle user data securely. A recent Microsoft survey found that ��58 percent of the public and 86 percent of business leaders are excited about the possibilities of cloud computing. But more than 90 percent of them are worried about security, availability, and privacy of their data as it rests in the cloud.��  
 
 

PROPOSED SYSTEM

    

           We propose an adaptive mobile video streaming and sharing framework, called AMES-Cloud, which efficiently stores videos in the clouds (VC), and utilizes cloud computing to construct private agent (subVC) for each mobile user to try to offer ��non-terminating�� video streaming adapting to the fluctuation of link quality based on the Scalable Video Coding technique. Also AMES-Cloud can further seek to provide ��nonbuffering��experience of video streaming by background pushing functions among the VB, subVBs and localVB of mobile users. We evaluated the AMES-Cloud by prototype implementation and shows that the cloud computing technique brings significant improvement on the adaptivity of the mobile streaming. We ignored the cost of encoding workload in the cloud while implementing the prototype. 
 

MODULE DESCRIPTION:

  1. Admin Module
  2. User1 Module
  3. User2 Module
  1. Admin Module: 

          In this module, Admin have three sub modules. They are,

        • Upload Video: Here Admin can add a new video. Its used for user for viewing more collections.
        • User Details: Admin can view the user those have regestred in this site.
        • Rate videos: This module for avoiding unexpected videos from users. After accept/reject videos then only user can/cannot view their own videos.
  1. User1 Module: 

          In this module, it contains the following sub modules and they are,

      1. News Feed: Here user of this social site can view status from his friends like messages or videos.
      2. Search Friends: Here they can search for a friends and send a request to them also can view their details.
      3. Share Video: They can share videos with his friends by adding new videos also they share their status by sending messages to friends.
      4. Update Details: In this Module, the user can update their own details.
  1. User2 Module: 
     

          In this module, user can register their details like name, password, gender, age, and then. Here the user can make friends by accept friend request or send friend request.

          They can share their status by messages also share videos with friends and get comments from them.. 
     
     

    System Configuration:- 

    H/W System Configuration:- 

            Processor               -    Pentium –III 

      Speed                                -    1.1 GHz

      RAM                                 -    256 MB (min)

      Hard Disk                          -   20 GB

      Floppy Drive                     -    1.44 MB

      Key Board                         -    Standard Windows Keyboard

      Mouse                                -    Two or Three Button Mouse

      Monitor                              -    SVGA

     

     

     S/W System Configuration:- 

      Operating System            : Windows95/98/2000/XP

      Application Server          :   Tomcat5.0/6.X                                 

      Front End                          :   HTML, Java, Jsp

      Scripts                                :   JavaScript.

      Server side Script             :   Java Server Pages.

      Database                            :   Mysql

      Database Connectivity     :   JDBC. 
       
       

      CONCLUSION 
       

            In this paper, we discussed our proposal of an adaptive mobile video streaming and sharing framework, called AMES-Cloud, which efficiently stores videos in the clouds (VC), and utilizes cloud computing to construct private agent (subVC) for each mobile user to try to offer ��non-terminating�� video streaming adapting to the fluctuation of link quality based on the Scalable Video Coding technique. Also AMES-Cloud can further seek to provide ��nonbuffering�� experience of video streaming by background pushing functions among the VB, subVBs and localVB of mobile users. We evaluated the AMES-Cloud by prototype implementation and shows that the cloud computing technique brings significant improvement on the adaptivity of the mobile streaming. The focus of this paper is to verify how cloud computing can improve the transmission adaptability and prefetching for mobile users. We ignored the cost of encoding workload in the cloud while implementing the prototype. As one important future work, we will carry out large-scale implementation and with serious consideration on energy and price cost. In the future, we will also try to improve the SNS-based prefetching, and security issues in the AMES-Cloud. 


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