29-09-2016, 11:37 AM
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Abstract :
This paper addresses the problem of optimizing the playback delay experienced by a population of heterogeneous clients, in video streaming applications. We consider a typical broadcast scenario, where clients subscribe to different portions
of a scalable video stream, depending on their capabilities.This paper examines the challenges that make simultaneous delivery and playback, or streaming, of video difficult, and explores algorithms and systems that enable streaming of ondemand video over packet networks such as the Internet.prediction-based resource algorithm is proposed that
INTRODUCTION.
Multimedia Streaming is constantly received by and presented to an end-user while being delivered by a provider. The term "streaming media" can apply to media other than video and audio as a better description for video on demand on IP networks. It is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. To achieve a high QoS for multimedia services, we propose a media-edge cloud (MEC) architecture, in which storage, central processing unit (CPU), and graphics processing unit (GPU) clusters are presented at the edge.
Over the past decade, more traffic is increased due to different forms of video (TV, Internet, File sharing using P2P, Video on Demand –VOD etc.,) Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services [2,19].Cloud multimedia services provide a capable, flexible, and scalable data processing method and offer a elucidation for the user demands of high quality and diversify multimedia.
Media Streaming is a type in which different types of media is constantly received by and presented to an end user which is being delivered by a provider. Now a days streaming of videos online has been in great demand. Almost every user watch the videos online but many a times it is difficult to watch the video without buffering as it is delivered directly from the centralized data servers and on this servers multiple user tries to watch the video online at the same time and it puts loads on the servers so sometime many users couldn’t watch it properly gets started with the buffering or also sometimes it is unable to access the video.
With the rapidly increased cloud-service scale and user scale [1], the data centers can hardly provide cloud services, e.g., Video-on-Demand (VoD) with guaranteed Quality of Service (QoS) to geodistributed users. Generally speaking, accessing multimedia video services through networks is no longer a problem. The major video platforms, such as Youtube and Amazon, have good management styles and provide users to share multimedia videos easily with diversified services.
No matter what the service is, users will always expect powerful, sound and stable functions. For multimedia videos, stability is of the greatest importance. To develop multimedia services provide a capable, flexible, and scalable data processing method and offer a elucidation for the user demands of high quality and diversify multimedia.
As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy everywhere network services.
Video streaming is not an issue in wired networks but wireless networks (mobile users) has been suffering from sharing of videos over limited bandwidth of links. Though 3G and LTE have been introduced to cope up with the bandwidth,the efforts were not successful due to rapid increase of mobile users.While receiving videos via 3G/4G mobile networks, users suffer from long buffering time to load video and interruptions due to limited bandwidth and link fluctuations. Thus, it is important to increase the quality of video streaming in mobiles using networking and computing resources effectively. Thus, it is vital to pick up the service quality of mobile video streaming while via the networking
and computing assets competently.
Cloud computing techniques are used to provide scalable resources to service providers to serve mobile users.Hence, clouds are used for large scale real time video services. Many Mobile cloud computing technologies have provided private agents for serving mobile users e.g.,Cloudlet. This is because, in cloud multiple threads can be created dynamically based on user demands.
The quality of mobile video streaming can be improved using two aspects:
• Scalability: Video Streaming service must be compatible with multiple mobile devices having various video resolutions, computing powers, wireless links and so
on. Capturing multiple bit rates of same video may increase the burden 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 name for the Annex G extension of the H.264/MPEG-4 AVC video compression standard.
• Adaptability: conventional video streaming method intended by considering relatively stable traffic links among servers and users achieve poorly in mobile
environment [2]. Thus the unpredictable wireless link status supposed to be accurately compact with to provide “tolerable” video streaming services.
Our main focus is to provide users with continuous streaming of videos without any buffering issues and also to save their money and provide them with good quality and high bandwidth videos.
RELATED WORKS:
STREAMING MEDIA CONTENT DELIVERY NETWORKS
The Internet has rapidly emerged as a mechanism for users to find and retrieve content, originally for webpages and recently for streaming media content delivery networks (CDNs) were originally developed to overcome performance problems for delivery of static web content (webpages). These problems include network congestion and server overload, that arise when many users access popular content. CDNs improve end-user performance by caching popular content on edge servers located closer to users. This provides a number of advantages. First, it helps prevent server overload,since the replicated content can be delivered to users from edge servers.Furthermore, since content is delivered from the closest edge server and not from the origin server, the content is sent over a shorter network path, thus
reducing the request response time, the probability of packet loss, and the
total network resource usage. While CDNs were originally intended for static
web content, recently, they are being designed for delivery of streaming media as well.
When Media Revolution Meets Rise of Cloud Computing which provides a cost-effective and powerful solution for the coming tide of the media consumption. Based on previous summary of the recent work on media cloud research, in this section, we first make some suggestions on how to build the media cloud, and then propose some potentially promising topics for future research.
Distributed Scheduling Scheme for Video Streaming over Multi-Channel Multi-Radio Multi-Hop Wireless Networks says for developing fully distributed scheduling schemes that jointly solve the channel-assignment, rate allocation, routing and fairness problems for video streaming over multi-channel multi-radio networks. Unlike conventional scheduling schemes focus on optimal system throughput or scheduling efficiency, our work aims at achieving minimal video distortion and certain fairness by jointly considering media-aware distribution and network resource allocation. Extensive simulation results are provided which demonstrate the effectiveness of our proposed schemes.
There are two kinds of adaptive streaming techniques based on whether adaptivity is controlled by the client or the server. Rate adaptation controlling techniques used TCP-friendly control methods for streaming services to detect the link quality so that adaptation can be done accurately.But by using this technique the servers have to always control which results in large workload. To overcome this issue,the H.264 Scalable Video Coding (SVC) technique has been introduced. Through this technique quality oriented scalable video can be delivered. The high quality videos can be achieved using cloud-based proxy because cloud computing improves the performance of SVC coding.
In the process of problem formulation, uncertain demand and uncertain cloud providers’ resource prices are considered. In contrast, the optimization problem formulated in our work takes into account a given probability distribution
function obtained from aforementioned studies for the prediction of media streaming demands. Furthermore,the problem of cost minimization is addressed by utilizing the discounted rates offered in the non-linear tariffs.
The whole video storing and streaming system in the cloud is called the Video Cloud (VC). In the VC, there is a largescale video base (VB), which stores the most of the popular video clips for the video Service Providers (VSPs).A temporal video base (tempVB) is used to cache new candidates for the popular videos, while tempVB counts the access frequency of each video.
The system model that we advocate in this paper for media streaming using cloud computing consists of the following components .
Media Owners.
Media Owners are uploads video files to cloud storage and send all the files to users via cloud storage and remove rejected files from cloud admin. View all the user status in our application.
Media Users
Media Users are access all the cloud admin approved files. Play all the videos without more streaming on cloud storage. View all the films available in cloud storage also in out application.
Cloud Admin
Cloud admin audit all the owners’ uploaded videos. First play the files and approve that files and maintain all the owners & users details. The Uploaded files is unwanted he can remove the file in cloud storage and intimate to proper owners.
Cloud Audit:
In this paper, we propose a clustering-based cloud node selection approach for communication-intensive cloud applications. By taking advantage of the cluster analysis, our approach not only considers the QoS values of cloud nodes, but also considers the relationship (i.e., response time) between cloud nodes. Our approach systematically combines cluster analysis and ranking methods. The experimental results show that our approach outperforms the existing ranking approaches.
Proposed system:
A video sequence consists of a sequence of video frames or images. Each
frame may be coded as a separate image, for example by independently
applying JPEG-like coding to each frame. However, since neighboring video
frames are typically very similar much higher compression can be achieved
by exploiting the similarity between frames. Currently, the most effective
approach to exploit the similarity between frames is by coding a given frame
by (1) first predicting it based on a previously coded frame, and then (2) coding the error in this prediction. Consecutive video frames typically contain the same imagery, however possibly at different spatial locations because of motion. Therefore, to improve the predictability it is important to estimate the motion between the frames and then to form an appropriate prediction that compensates for the motion.
The process of estimating the Chapter 1 motion between frames is known as motion estimation (ME), and the process of forming a prediction while compensating for the relative motion between two frames is referred to as motion-compensated prediction (MC-P).Block-based ME and MC-prediction is currently the most popular form of ME and MC-prediction: the current frame to be coded is partitioned into 16x16-pixel blocks, and for each block a prediction is formed by finding the best-matching block in the previously coded reference frame. The relative motion for the best-matching block is referred to as the motion vector.
There are three basic common types of coded frames: (1) intra-coded frames,
or I-frames, where the frames are coded independently of all other frames,
(2) predictively coded, or P-frames, where the frame is coded based on a
previously coded frame, and (3) bi-directionally predicted frames, or B-
frames, where the frame is coded using both previous and future coded
frames. Figure 1 illustrates the different coded frames and prediction
dependencies for an example MPEG Group of Pictures (GOP). The selection
of prediction dependencies between frames can have a significant effect on
video streaming performance, e.g. in terms of compression efficiency and
error resilience.
Video Delivery via Streaming:
Video delivery by video streaming attempts to overcome the problems
associated with file download, and also provides a significant amount of
additional capabilities. The basic idea of video streaming is to split the video
into parts, transmit these parts in succession, and enable the receiver to
decode and playback the video as these parts are received, without having to
wait for the entire video to be delivered. Video streaming can conceptually be
thought to consist of the follow steps:
1) Partition the compressed video into packets
2) Start delivery of these packets
3) Begin decoding and playback at the receiver while the video is still
being delivered .
Video streaming enables simultaneous delivery and playback of the video.
This is in contrast to file download where the entire video must be delivered
before playback can begin. In video streaming there usually is a short delay
(usually on the order of 5-15 seconds) between the start of delivery and the
beginning of playback at the client. This delay, referred to as the pre-roll delay, provides a number of benefits.