STM Article Repository

In the past few years, the video streaming services are gaining more popularity over the mobile networks. As a result, the demands of video streaming services have been increasing over the mobile networks; hence the capacity of wireless links cannot keep up with the traffic needs. This diversity between link capacity and the traffic demands along with the time-varying wireless link condition lead to inadequate service quality of video streaming, like greater loading or buffering delays and sporadic interruptions. In this paper, with the influence of the present mobile cloud computing technology, we proposed the ESVP-Cloud architecture (i.e. Efficient Social-aware Video Prefetching) for mobile video streaming and sharing. The proposed system is mainly divided in two parts such as the cloud-assisted adaptive video streaming with efficient social video sharing (ESVS) and social-aware video prefetching (SAVP). To provide video streaming services efficiently using ESVP-Cloud architecture the personalized private agent (i.e. subVC) is constructed dynamically in the video cloud (VC) by ESVS and SAVP for each active mobile user. The bandwidth scalability is properly handled by scalable video coding (SVC) technique by adaptively adjusting the streaming flow based on the feedback of the link quality, to offer a “non-terminating” adaptive multimedia streaming. Likewise, ESVS monitors the social activities among different mobile users; in order to provide the “non-buffering” multimedia streaming by background prefetching based on interaction of the users in SNSs. To serve these functionality users private agents can efficiently share the videos and try to prefetch video data segments in advance. The ESVP-Cloud architecture is proposed to demonstrate its performance, which shows that mobile cloud computing will improve the quality of multimedia streaming and effectively offers the adaptive streaming. Furthermore, it is shown that proposed system will reduces the large portion of the traffic load (nearly 50%-80%) by reducing the redundant downloads, also it reduces the access delay of videos to match the user demands, by performing video sharing and efficient social-aware video prefetching significantly facilitate by cloud computing.