adaptive and efficient video streaming and sharing for ...

ADAPTIVE AND EFFICIENT VIDEO STREAMING AND SHARING FOR MOBILE USERS USING CLOUD ASSISTANCE 1

SARBOJIT BANERJEE, 2ROHEET BHATNAGAR

1

Mtech, Software Engineering, 2Professor & HOD, Dept. Of Computer Science & IT, Manipal University, Jaipur E-mail: [email protected], [email protected]

Abstract- Due to the high demands of video traffics over mobile networks, the wireless link capacity fails to keep up the pace with the demand. There exits a gap between the demand and the link capacity which results in poor service quality of the video streaming over mobile networks which includes disruptions and long buffering time. Following the cloud computing technology, we suggest two solutions : adaptive video streaming and social video sharing over mobile networks. A private agent is constructed for each mobile user in the cloud which adjust the video bit rate using scalable video coding technique based on the return value of the wireless link condition. And, the mobile social interaction is for each user is checked by the agents so that the videos will be prefetched in advance in order to share them effectively. We have implemented the idea over desktop machines and we are trying to implement the same over mobile networks. Index terms- Agent, cloud computing, networks, svc, video streaming, video sharing.

for the users who have not seen the video yet. It will be done by the background job supported by the agents of cloud so when the user clicks the video, it will start playing instantly.

I. INTRODUCTION Due to the fast development of the mobile communication technology, more people are getting addicted to video streaming over phones. Over the few years, video streaming is becoming challenging over wireless links than on wired links. The increasing video traffic demands are overwhelming the wireless link capacity.

In this paper we have designed and implemented an adaptive video streaming and effective sharing of videos framework for mobile users. Here it constructs a private agent for each mobile users in cloud computing environments. For adaptive video streaming, we adjust the bit rate for each users using scalable video coding. The private agents will keep track of the feedback information on the link status. They are dynamically initiated and optimized in the cloud computing platform. For the efficient video sharing part the users are provided with the instant playing of shared video by prefetching the video clips in advance from the private agent to the local storage of the mobiles. The strength of the social links between mobile users and the past history of the various social activities will help in determine probabilistically which and how much video will be prefetched.

The mobile users often suffer from disruptions and very long buffering time while receiving video through networks like 3G or 4G due to short bandwidth and link fluctuations. So, it is imperative to improve the services of video streaming over mobile networks. Scalability and adaptability are the two aspects in order to improve the quality of the video streaming over mobile networks. Scalable video coding (SVC) and adaptive streaming techniques can be combined together to accomplish the best possible quality of the video streaming services. So, that we can adjust the SVC layers which depends on the current link status.

The rest of the paper includes related works in section II. Section III,IV & V includes cloud framework,social aware video prefetching and implementation. Section VI includes conclusion and references at the last.

The cloud computing technique is ready to provide scalable resources to the service providers and process offloading to the mobile users. So, cloud data centers can provision to large scale real time video services. In cloud more than one agent instances can be maintained dynamically and effectively due to mobile user demands.

II. RELATED WORKS In the type of adaptive streaming, the traffic rate is adjusted so that the mobile user will experience maximum quality of video which is based on the bandwidth capacity [2]. The Smooth Streaming of Microsoft is a online streaming service which is adaptive and can change or switch different bit rate segments rates and resolution of video at servers, while on the other hand clients dynamically request

The social network services (SNS) on the mobile networks is becoming increasingly popular. In SNS’s mobile users might post, comment and share videos which can be viewed and by his/her friends. So, we are inspired to exploit the relationship between the mobile users and their SNS activities in order to prefetch the first part of the video during streaming

Proceedings of SARC-IRF International Conference, 12th April-2014, New Delhi, India, ISBN: 978-93-84209-03-2 47

Adaptive and Efficient Video Streaming And Sharing for Mobile Users Using Cloud Assistance

videos based on the local monitoring of the link quality. Companies like Apple and Adobe have developed client-side adaptive online streaming solutions which works in the same fashion. There are also some similar adaptive streaming services where servers controls the adaptive transmission of video segments, for example, the Quavlive Adaptive Streaming.

encoding at PC servers. The work in the reference [10] includes a quality level scalable video execution by SVC, but it is only tested in a LTE Network. As per the performance of encoding of SVC, CloudStream is developed to deliver good quality video streams by a cloud-based proxy of SVC [11]. The cloud computing is being well equipped to provide streaming services of videos, specially in the wired connection of internet due to its capability and scalability [12]. Like for example, the VoD streaming which is quality-assured auto-scaling of bandwidth is based on the cloud computing is proposed in the reference [13], and the CALMS designed framework [14] is an online streaming service which is cloud assisted for globally distributed users.

Regarding rate adaptation controlling techniques, transmission control protocol (T CP) rate control techniques for streaming services on the mobile networks were proposed [3], [4]. A rate adaptation algorithm for conversational 3G video streaming is introduced by [5]. Then, a few cross-layer adaptation techniques are discussed [6], [7], which can acquire more accurate information of link quality so that the rate adaptation can be more accurately made. Recently the H.264 Scalable Video Coding (SVC) technique has gained a momentum [8]. An adaptive video streaming system based on SVC is deployed in [9], which studies the real-time SVC decoding and

More recently, new designs for users on top of mobile cloud computing environments are proposed, which virtualize private agents that are in charge of satisfying the requirements (e.g., QoS) of individual users such as Cloudlets [15] and Stratus [16].

Figure 1 : An illustration of the framework

user. A sub video base (subVB) is present in subVC and it stores segments of recently fetched video. The subVC contains encoding functions, and if the mobile user demands a new video, which is not in the subVB or the VB in VC, the subVC will fetch, encode and transfer the video. During the time of the streaming of videos, the users of the mobile will report the link conditions to the subVC and it will offer adaptive streams. There is a temporary storage in every mobile devices which is known as local video base ( localVB) , used for prefetching and buffering.

III. CLOUD FRAMEWORK As shown in the figure Fig. 1, the video streaming and storing system in the cloud is called video cloud (VC). Within the video cloud, there is video base (VB), which is responsible to store the popular video clips. tempVB is a video base which is temporary and is utilized to cache new mobile users for popular videos, while it counts the access frequency of each video.VC keeps on executing a collector to look for videos which are popular already in video service provider (VSP), and it will re-encode the videos that are collected into scalable video coding format and will save in tempVB.

IV. SOCIAL AWARE VIDEO PREFETCHING

A sub video cloud (subVC) is dynamically created if there is any ling of video demand from the mobile

In social network services, the mobile users can subscribe to their friends and content publishers, and


Adaptive and Efficient Video Streaming And Sharing for Mobile Users Using Cloud Assistance

there are numerous types of activities socially. So it is required for us to define different kinds of levels of strengths for those socially oriented activities to indicate many different possibilities so that the videos that are shared by one mobile user can be viewed by the receiver of his/her sharing activities, so the sub video clouds may engage into prefetching at subVB done in background and may transfer to mobile user’s localVB. Because after one shares a video, there can be a bit of delay that the receiver will know the sharing, and starts to watch. So, advance prefetching will not affect the mobile users in most of the cases. But a mobile user may play the videos to view without any delay due to buffering as the first part or may even the entire video is locally prefetched already.

changes of quality of links which depends on scalable video coding technique, and to try to provide “nonbuffering” video streaming experience by background prefetching based on the tracking of the interactions of mobile users in their SNSs. We evaluated the framework by prototype implementation, and showed successfully that the cloud computing method brings improvement to the adaptability and scalability of the mobile streaming, and the efficiency of intelligent prefetching. REFERENCES

There are three kinds of social activities in SNS:Direct recommendation: In SNSs, a user can directly recommend a video to particular friend(s) by a short notice message. The recipients of the message may watch it with very high probability. This is considered as “strong”. Subscription: A mobile user can subscribe to a particular video publisher based on interests. This interest-driven connectivity between the subscriber and the video publisher is considered as “median,” because the subscriber may not always watch all subscribed videos. Public sharing: The activity of watching or sharing a video by a user can be seen by his/her Friends in their timeline of activity stream. We consider this public sharing as a “weak” connectivity among users, because many people may not watch the video that one has watched or shared with no specific recommendation.

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V. IMPLEMENTATION As per the design of the framework we have implemented the social network system as a site with the help of IDE as Microsoft visual studio 2010 ultimate and used the database as sql sever 2008. We have written codes in asp.net mainly. We have successfully achieved the video streaming adaptively, following the SVC coding and effective sharing of videos as well. However the formats of videos can be played are only in mp4 and flv. Below is a figure which illustrates the execution of a mp4 video.

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CONCLUSION

[13] 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.

Here we have discussed our proposal of the cloudassisted adaptive mobile video streaming and socialaware prefetching, which stores the videos efficiently in the clouds and constructs private agent (subVC) for active mobile users in order to try to give “nonterminating” streaming of videos by adapting to the

[14] F. Wang, J. Liu, and M. Chen, “CALMS: Cloud-assisted live media streaming for globalized demands with time/region diversities,” in Proc. IEEE INFOCOM, 2012, pp. 199–207.


Adaptive and Efficient Video Streaming And Sharing for Mobile Users Using Cloud Assistance [15] N. Davies, “The case for VM-Based Cloudlets in mobile computing,” IEEE Pervasive Computing, vol. 8, no. 4, pp. 14–23, 2009.

[16] B. Aggarwal, N. Spring, and A. Schulman, “Stratus: Energy-efficient mobile communication using cloud support,” in Proc. ACM SIGCOMM, 2010, pp. 477–478.

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