ADAPTIVE MOBILE VIDEO STREAMING AND SHARING USING CLOUD COMPUTING

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1 ADAPTIVE MOBILE VIDEO STREAMING AND SHARING USING CLOUD COMPUTING 1 ASHALA VIJAY KUMAR, 2 D. PHANI KUMAR 1 Department of CSE, Sreenidhi Institute of Science and Technology, India. phani123d@gmail.com 2 Department of CSE, Sreenidhi Institute of Science and Technology, India. vijaykumar.feb13@gmail.com Abstract In mobile networks the video traffic has been incrementing highly but the wireless link capacity cannot keep up with the traffic so that wireless link capacity cannot gratifying the ordinant dictations of mobile utilizer due to the variation in the link status and many disruptions, it causes long time buffering to mobile utilizer. This gap results in poor accommodation quality of video streaming over mobile networks. To overcome from this a framework is proposed for the efficient utilization of wireless link capacity by the amalgamation of Adaptive mobile video streaming [AMoV] and Efficient social video sharing [ESoV] techniques utilizing cloud. A private agent is constructed for each mobile utilizer in the cloud which adjusts the video bit rate utilizing scalable video coding technique predicated on the return value of the wireless link condition. And, the mobile gregarious interaction is for each utilizer is checked by the agents so that the videos will be prefetched in advance in order to share them efficaciously. We have implemented the conception over desktop machines and we are endeavoring to implement the same over mobile networks. Index Terms Efficient social video sharing, networks, video streaming, video sharing, uninterrupted streaming, On- line social net works. I INTRODUCTION Cloud computing era technology reigns with advancements, that provides sundry accommodations to the human s need and withal it urges the more indispensability for the implementing technology. It provides a platform for other upcoming technologies like immensely colossal data, mobile computing to inculcate its accommodation and provide the QoS to the customers. All the accommodations that are provided to the customer are done utilizing could as their backbone, it gives astronomical amount of resources and infrastructure to consumer who acts as vendors to minute scale business and cloud could provide accommodations to plenarily fledged organization with less cost. Organizing the accommodation and elongating the accommodation depending upon the growing needs of the customer could be achieved. Over the past decade, increasingly more traffic is accounted by video streaming and downloading. In particular, video streaming accommodations over mobile networks have become prevalent in earlier days of cloud computing. While the video streaming is not a big deal of challenge in wired networks, in mobile networks it has been suffering from video traffic transmissions over scarce

2 bandwidth of wireless links. Against of network operators desperate exercise to enhance the wireless link bandwidth (e.g., 3G and LTE), soaring video traffic demands from mobile users are rapidly inundating the wireless link capacity. While taking video streaming traffic through 3G/4G mobile networks, mobile users often phasing a problem for huge buffering time and disruptions due to the circumscribed bandwidth and link condition fluctuation caused by multipath fading and utilizer mobility. Thus, it is crucial to ameliorate the accommodation quality of mobile video streaming while utilizing the networking and computing resources efficiently. II TARGET OPERATIONS Scalability: Video Streaming accommodation must be compatible with multiple mobile contrivances having sundry video resolutions, computing potencies, wireless links and so on. Capturing multiple bit rates of same video may increase the encumbrance on servers in terms of storage and sharing. To resolve this issue, the Scalable Video Coding (SVC) technique has been introduced. Scalable Video Coding (SVC) is the designation for the Annex G addition of the H.264/MPEG-4 AVC video confining standard. SVC standardizes the cipher of a high-quality video bit stream that additionally contains one or more subset bit streams. A subset video bitstream is derived by dropping packets from the more astronomically immense video to reduce the bandwidth required for the subset bitstream. A subset bitstream can locate a low spatial decision, or a lower temporal decision, or a lower quality video signal (each discretely or in amalgamation) examine to the bitstream it is copied from. The following procedures are possible: Temporal (frame rate) scalability: The kineticism emolument dependencies are structured so that consummate pictures (i.e. their associated packets) can be dropped from the bit stream. Spatial (picture size) scalability: Video is coded at multiple spatial decisions. The data and decoded samples of low resolutions can be addicted to prognosticate data or samples of higher resolutions in order to reduce the bit rate to code the higher resolutions. SNR/Quality/Fidelity scalability: Video is coded at a single spatial resolution but at several qualities. The data and decoded samples of lower qualities can be habituated to presage data or samples of higher qualities in order to reduce the bit rate to code the higher qualities. Combined scalability: A amalgamation of the 3 scalability modalities described above. Adaptability: Traditional video streaming techniques developed by considering relatively stable traffic links in between servers and users perform few times only in mobile environments. Thus the fluctuating wireless link status should be opportunely dealt with to provide tolerable video streaming accommodations. To address this issue, we have to adjust the video bit rate acclimating to the currently time-varying available link bandwidth of each mobile utilizer. Such adaptive streaming technique scan efficaciously reduces packet losses and bandwidth waste. Cloud computing techniques are habituated to provide scalable resources to accommodation providers to accommodate mobile users. Hence, clouds are utilized for sizably voluminous scale authentic time video accommodations. Many Mobile cloud computing technologies have provided private agents for accommodating mobile users e.g., Cloudlet. This is because, in cloud multiple threads can be engendered dynamically predicated on utilizer demands. Social Network Accommodations (SNS s) have occupied a major role recently. In SNS s user can share, comment, and post the videos among friends and groups. Users can follow their favourites depending on their interest in which their

3 adherents are liable to optically canvass popular person posts. E.g., Twitter, Facebook. III PROPOSED WORK We implement 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 utilizer to endeavor to offer non-terminating video streaming habituating to the fluctuation of link quality predicated on the Scalable Video Coding technique. Also AMES-Cloud can further investigate to give nonbuffering experience of video streaming by background pushing functions among the VB, subvbs and localvb of mobile users. We check the AMES-Cloud by prototype developing and displays the cloud computing technique provides paramount amendment on the adaptivity of the mobile streaming. We forget the cost of encoding workload in the cloud while developing the prototype. IV MODULE DESCRIPTION: 1. Admin Module: In this module, Admin have three sub modules. They are, Upload Video: Here Admin can integrate an incipient video. Its utilized for utilizer for viewing more accumulations. User Details: Admin can view the utilizer those have registered in this site. Rate videos: This module for evading unexpected videos from users. After accept/reject videos then only utilizer can/cannot view their own videos. and send a request to them withal can view their details. Share Video: They can apportion videos with his friends by integrating incipient videos withal they apportion their status by sending messages to friends. Update Details: In this Module, the utilizer can update their own details. 3. User2 Module: In this module, utilizer can register their details like designation, password, gender, age, and then. Here the utilizer can make friends by accept friend request or send friend request. They can apportion their status by messages withal share videos with friends and get comments from them. V EXPERIMENTAL RESULTS: Login as different users then, send and accept the requests from multiple users. So that they will be become friends to each other. Repeat the above process until each user will become friend with other user. After adding the friends, upload the videos. Upload video screen for user: 2. User1 Module: In this module, it contains the following sub modules and they are, News Feed: Here utilizer of this gregarious site can view status from his friends like messages or videos. Search Friends: Here they can probe for a friends

4 After running the cloud server, run the client application. Click on View Clients button to see the number of clients (Users). Select mobile clients (user), then it will shows the videos of type tagged videos, public videos and subscribed videos for the particular user. Here selected mobile client as bob. Client bob downloading a tagged video(direct recommendation video file). After downloading the video: 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 nonterminating 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. Conclusion of this paper, review of our proposal of an adaptive mobile video streaming and sharing framework using cloud computing, called AMES-Cloud, which efficiently stores videos in the clouds (VC), and utilizes cloud computing to construct private agent (subvc) for each mobile utilizer to endeavor to offer non-terminating video streaming habituating to the fluctuation of link quality predicated on the Scalable Video Coding technique. Withal 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 paramount amendment on the adaptivity of the mobile streaming. The focus of this paper is to verify how cloud computing can ameliorate the transmission adaptability and prefetching for mobile users. We ignored the cost of encoding workload in the cloud while implementing the prototype. As one paramount future work, we will carry out sizably voluminous-scale implementation and with earnest consideration on energy and price cost. In the future, we will withal endeavor to amend the SNS-predicated prefetching, and security issues in the AMES-Cloud. REFERENCES [1] M. Wien, R. Cazoulat, A. Graffunder, A. Hutter, and P. Amon, Real-Time System for Adaptive Video Streaming

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