The transmission of data has grown over years in all the streams of technology. Video and image data plays a very important position in communication around the globe. The usage of Medias over mobile devices had exploded years ago in technology. However, the usage of traditional network connecting protocols and the service providers are providing lack of quality in services. As the number of users who uses mobile phones is increasing day by day the video traffic over network is also increasing thereby causes disruption in the service which is caused by low bandwidth. Due to this disruption the wireless cannot able to satisfy the users demand for video streaming which eventually causes long buffering time. Influencing cloud computing knowledge to gain advantage over this issue we suggest two solutions. i) Mobile Video Streaming (MoV) and Social Video Sharing (SoV). MoV will create a private cloud for each mobile user which adjusts the bit rate based on return value using scalable video coding technique to improve the scalability and efficient utilization of bandwidth. SoV uses the agent to pre fetch the video data for effective sharing and to reduce the buffering time.

1. Journal for Research| Volume 02 | Issue 08 | October 2016
ISSN: 2395-7549
All rights reserved by www.journalforresearch.org 17
A Framework for Mobile Video Streaming and
Video Sharing in Cloud
Prabhu K Prabhu R
Assistant Professor Assistant Professor
Department of Computer Science & Engineering Department of Computer Science & Engineering
V.S.B College of Engineering Technical Campus, Coimbatore V.S.B College of Engineering Technical Campus, Coimbatore
Krishna Kumar R Sangeetha S
Assistant Professor Associate Professor
Department of Computer Science & Engineering Department of Computer Science & Engineering
V.S.B College of Engineering Technical Campus, Coimbatore V.S.B College of Engineering Technical Campus, Coimbatore
Abstract
The transmission of data has grown over years in all the streams of technology. Video and image data plays a very important
position in communication around the globe. The usage of Medias over mobile devices had exploded years ago in technology.
However, the usage of traditional network connecting protocols and the service providers are providing lack of quality in
services. As the number of users who uses mobile phones is increasing day by day the video traffic over network is also
increasing thereby causes disruption in the service which is caused by low bandwidth. Due to this disruption the wireless cannot
able to satisfy the users demand for video streaming which eventually causes long buffering time. Influencing cloud computing
knowledge to gain advantage over this issue we suggest two solutions. i) Mobile Video Streaming (MoV) and Social Video
Sharing (SoV). MoV will create a private cloud for each mobile user which adjusts the bit rate based on return value using
scalable video coding technique to improve the scalability and efficient utilization of bandwidth. SoV uses the agent to pre fetch
the video data for effective sharing and to reduce the buffering time.
Keywords: Cloud Computing, Mobile Video Streaming, Video Coding, H.264/AVC
_______________________________________________________________________________________________________
I. INTRODUCTION
The more innovations that were made in the wireless network made the people of this era to make use of wireless phones which
results in traffic in the network. As many people are preferring video streaming over mobile phones it is challenging for the
service providers to provide quality of video service in wireless than in the wired network. In addition, the number of bits stored
and transmitted some five years back is tripled in the recent year which eventually causes traffic in the wireless network. The
mobile users often suffer from disruptions and long buffering due to low bandwidth and the fluctuations in the 3G/4G wireless
network. Mobile cloud computing provides with the expected advantages like large storage capacity, high bandwidth, increase
in the QoS, performance. Even then the traffic is accounted by video streaming and mobility. Hence, recently there have been
several studies on how to improve the service quality of mobile video streaming on two aspects i) scalability–mobile video
streaming should support wide spectrum of mobile devices. It should support different video resolutions with different
computations on various wireless links such as 3G and LTE. ii) Adaptability - the link has to adjust the video bit rate adapting to
the currently time-varying available link bandwidth of each mobile user. Cloud constructs a private agent for each mobile user in
cloud surroundings, which is used by its two ways: Mobile video streaming and social video sharing. By facilitating a 2-tier
structure cloud supports efficient video sharing. First tier is a content delivery network and second tier is a data center.
II. RELATED WORK
In the adaptive video streaming the speed of the video streaming is based on the traffic and the link’s variation in the bandwidth.
This streaming can be done in two ways. Primarily on the server side where Microsoft’s streaming is the primary one. It is a flat
or smooth streaming which dangles between the disparate bit streams and the configurable bit stream and video resolution at the
server end while the client dynamically request for the video by locally monitoring the link’s quality. Apple and Adobe are the
live adaptive client side streaming which uses the latter way of streaming. The stereotypical streaming is measured by the
conventional rate of bandwidth in the stable internet. The mobile video stream can be disrupted due to packet failure and
bandwidth misuse. Concerning price adaptation calculating method, TCP friendly rate control is projected for providing
streaming services over the mobile systems. Where TCP throughput stream is calculated as a function of packet loss rate round
trip time, and packet size. To achieve effective streaming H.264/ASV is collaborated with the extension SVC.

2. A Framework for Mobile Video Streaming and Video Sharing in Cloud
(J4R/ Volume 02 / Issue 08 / 004)
All rights reserved by www.journalforresearch.org 18
Fig. 1: Comparison of traditional video streaming and SVC
But it is tested only on the LTE network. Concerning the performance of SVC cloud stream mainly forth put the high quality
streaming videos through cloud based SVC proxy.
Mobile Cloud Computing Techniques
The cloud computing has been well positioned to provide video streaming services, especially in the wired Internet because of its
scalability and capability. To extent the cloud service to mobile phones through wireless networks we need to consider some
factors like mobility, storage, and wireless dynamics. Moreover, many recent developments are made in the design of cloud
computing which are in charge of satisfying the individual user’s requirement.
III. EXISTING SYSTEM
The SVC is used for video conferencing as well as for mobile to broadcast video and for editing applications. Saving different
versions of same video may create overhead in the means of storage and communication. To overcome this, we use SVC with
H.264/AVC. In the existing system when the user types the URL in the mobile browser the page navigates to the respective
URL. If that page has a video embedded in the URL it starts to stream based on the strength of the signal it gets streamed. If the
resolution is high or HD it will take long time to stream or gets paused. In the mentioned situation there is delay in the requested
video. The drawbacks of these issues are it always uses it uses maximum link capacity for streaming and it cannot control the
resolution. In case of weak signal user cannot see the video maintaining consistency as it gets paused on the screen till video
streams.
IV. PROPOSED SYSTEM
Keeping above objectives in mind we are proposing dubbed AMES cloud in this system to achieve adaptive video streaming.
AMES cloud provides a private agent in cloud for each mobile user. The private agent of a mobile user keeps track of the
feedback information about the status of link. In the cloud Platform the private agents are initiated dynamically and optimized.
Also the real-time scalable video coding is done on the cloud computing side efficiently.
The streaming of video and storing it in the cloud is known as video cloud (VC). Within the video cloud there is a base to store
the most popular videos called video base (VB). Temp base is a kind of VB which is specifically used to catch the popular videos
only for new mobile users. It uses a special counter to count the access frequency of each video. VC will execute a collector to
identify the most popular videos in the Video Service Provider (VSP) and encode it in the SVC format and it will get saved in
temp VB. The sub VC is created in order to line up the demand for the video from the mobile users. Sub VB is implemented
inside the sub VC to provide the most recently accessed video in the cache. If the user requests for a video which is not in the VB
or sub VB it is the responsibility of VC or sub VC to fetch, encode and move the video to the mobile under streaming.

3. A Framework for Mobile Video Streaming and Video Sharing in Cloud
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Fig. 2: Illustration of proposed framework
During the time of the streaming of videos, the mobile of the user will automatically send the report on the link condition to
the sub VC and it will offer adaptive streams. The temporary storage which is unique in AMES framework is local video base
(local VB) which is used for prefetching and buffering.
V. CONCLUSION AND FUTURE WORK
A In this paper we have discussed about our proposal on AMoV which is achieved through sharing the AMES framework using
private agent in the cloud. VC and sub VC stores the video to provide “Non terminating” video which are explicitly used to
adapt to the fluctuations in the link capacity. It also provides “Non Buffering” service with the help of VB, sub VB and local VB
based on Scalable Video Coding technique. The main objective of the paper is to know how the cloud helps the video streaming
in mobile to achieve adaptability and scalability. But this paper turns a blind eye towards cost while encoding. To overcome
this detract we propose large scale implementation primarily considering cost and power. We try to improve prefetching and also
to conclusion section is not required. Although a conclusion may review the main points of the paper, do not replicate the
abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions.
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