Review on AMES-Cloud Using Preservation, Fetching and Decisive Video Streaming Over Cloud Computing

The video traffic demands are increasing over a mobile network through wireless link cannot corporate with the demand of video traffics. The increasing traffic demand is accounted by video streaming and downloading. Hence, there is a gap between link capacity and traffic demands along with the time varying condition which is result in the poor quality of video streaming service over a mobile network such as sending long buffering time and intermittent disruptions due to limited bandwidth and link condition. By leveraging cloud computing technology, we propose a new mobile video streaming framework which has two main parts : Efficient social video sharing and Adaptive mobile video streaming which built a private agent which provides video streaming service for each mobile user in the network efficiently. To demonstrate its performance we implement a prototype of AMES-Cloud framework. Thus, it is crucial to improve the video quality service of streaming while using the computing resource and networking efficiently and also provides preservation over cloud computing. DOI: 10.17762/ijritcc2321-8169.15010

A Framework of Video Bit-Stream Scalability and Efficient, Secure Social Video Sharing in the Clouds

No Abstrac

A Structure of Adaptive Portable Video Streaming and Efficient Social Video Distribution in the Clouds

Recently there have been many studies on how to improve the service quality of mobile video streaming  on cloud computing. Resilience against cloud provider failure and temporary unavailabilityUse multiple cloud providers to construct a reliable cloud storage service out of unreliable components.we need Efficient proofs of file availability by interacting with cloud providers but these systems are not sufficient for this type of environments. Use multiple cloud providers to construct a reliable cloud storage service out of unreliable components. High availability and tolerance to adversarial failures.In this paper we address the problems of adaptive cloud services. Our adversaries are the cloud service provider itself, its insiders, or any third party attacks who are able to view the target data, monitor query processing on the data, obtain or infer the data and queries. – have complete access to database server While we need to provide security and privacy against these adversaries, we cannot omit functionality and performance – the most important property for a data service

Peripatetic Shared TV Using Cloud

While demands on video traffic over Portable 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 Portable networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new Portable video streaming Structure, dubbed AMES-Cloud, which has two main parts: AMoV(adaptive Portable video streaming) and ESoV(efficient social video Distribution). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each Portable 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 Portable users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud Structure to demonstrate its performance. It is shown that the private

A productive video sharing and streaming in cloud environment

While requests on video movement over mobile networks have been souring, the remote connection limit can't stay aware of the activity request. The crevice between the movement request and the connection limit, alongside time-changing connection conditions, results in poor administration nature of video gushing over portable systems, for example, long buffering time and discontinuous interruptions. Utilizing the distributed computing innovation, we propose another portable video gushing structure, named AMES-Cloud, which has two primary parts: AMoV (versatile video spilling) and ESoV (productive social video sharing). AMoV and ESoV develop a private specialists to give video spilling benefits productively to every versatile client. For a given client, AMoV gives her a chance to private specialists adaptively modify her spilling stream with a versatile video coding method in view of the criticism of connection quality. In like manner, ESoV screens the informal community collaborations among versatile clients, and their private specialists attempt to prefetch video content ahead of time. We actualize a model of the AMES-Cloud structure to exhibit its execution. It is demonstrated that the private operators in the mists can adequately give the versatile gushing, and perform video sharing (i.e., prefetching) in view of the informal organization investigation

An adaptive system for real-time scalable video streaming with end- to-end qos control

This paper presents a real-time adaptive video streaming system based on the latest standardized video codec H.264/MPEG-4 AVC scalable extension (SVC). The system provides a full MPEG-21 media access framework over heterogeneous networks and terminals with end-to-end QoS control and multimedia adaptation based on SVC. This adaptive streaming system is composed of a server with a real-time SVC encoder, an adaptive network node, and a terminal with appropriate feedback of perceptual quality, network conditions and user preferences for adaptation support. The system facilitates a general content adaptation solution to achieve the end-to-end QoS control

Spatial scene adaptation in broadcast environment

The heterogeneity of handheld terminals in terms of screen resolution, processing capabilities or available decoding memory is a challenge for multimedia services that have been tackled by many scene adaptation techniques so far. In broadcast environment, the adaptation intelligence must be transmitted along with the content and may induce critical costs that must be minimized. In this paper, we propose a broadcast-friendly adaptation technique of the spatial layout of multimedia content based on the use of incremental scene updates. The advantages of our approach have been evaluated on a T-DMB digital radio service and compared to other adaptation techniques applicable to broadcasted multimedia services. Experimental results show that fine-grained spatial adaptation on constrained handheld terminals can successfully be achieved through adaptation scene updates with a limited bandwidth overhead

Providing Best Quality Of Video Streaming Using AMES Method

In wireless networks(mobile networks) the wireless link capacity cannot be stable with the demand of traffic over the networks. Due to the gap between the traffic demand and the link capacity it results in poor service quality of video streaming over mobile networks. Long buffering time and intermittent disruptions results in poor service quality of video streaming. Hence we propose a new mobile video streaming framework, dubbed AMES-Cloud, which consists of two main parts namely AMoV (adaptive mobile video streaming) and ESoV(efficient social video sharing). To provide efficient video streaming services for each mobile user AMoV and ESoV construct a private agent. AMoV lets her private agent adaptively adjust her streaming flow with scalable video coding technique based on the feedback of link quality. Likewise ESoV lets her private agents to pre-fetch video content in advance and also monitors the social network interactions among mobile users

Scalable video streaming with automatic content adaptation

Scalable Video Coding technology enables flexible and efficient distribution of videos through heterogeneous net- works. In this regard, the present work proposes and evaluates a method for automatically adapting video contents, according to the available bandwidth. Taking advantage of the scalable video streams characteristics, the proposed solution uses bridge firewalls to perform adaptation. In brief, a scalable bitstream is packetized by assigning a different Type of Service field value, according to the corresponding resolutions. Packets corresponding to the full video resolution are then sent to clients. According to the given bandwidth constraints, an intermediate bridge node, which provides Quality of Service functionalities, eventually discards high resolutions information by using appropriate Priority Queueing filtering policies. A real testbed has been used for the evaluation, proving the feasibility and the effectiveness of the proposed solution