Versatile Video Sustaining Service

Versatile Video Sustaining Service

1. R.Ramya 2.R.Sella Meenal 3.O.R.Shruthi 4.B.Sorna Priyanka (IV-year), Department of Information Technology

Velammal College of Engineering and Technology, Madurai-9.

Abstract -To develop multimedia services which provides a capable, flexible, and scalable data processing method and offer an elucidation for the user demands of high quality and diversify multimedia. As rational mobile phones and wireless networks become higher popular, web services for customers are no longer finite to the central. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy everywhere network services.To estimate the performance of the proposed algorithm, to implement a test bed using the android mobile phone and the Scalable Video Coding (SVC) codec. The results demonstrate the probability and effectiveness of the proposed adaptation algorithm for mobile video streaming applications in android application. The multimedia video file is transmitted to the mobile device through the service.

Index Terms: QoS, SVC(Scalable Video Coding)

1. Introduction

   Cloud multimedia services provide a capable, flexible, and scalable data transferring method and action an elucidation for the customer demands of huge quality and diversify multimedia. Generally speaking, achieve multimedia video sustain through networks is no protracted a complication. The major broadcast platforms, such as YouTube and Amazon, have good administration styles and provide customers to share multimedia videos easily with different services. No matter what the service is, customers will always expect capable, robust and stable functions. For multimedia videos, stability is of the greatest importance. As rational mobile phones and wireless networks become higher popular, web services for customers are no longer finite to the central Multimedia information can be access easily using mobile devices, allowing customers to enjoy ubiquitous web services. Considering the defined bandwidth available for mobile sustaining and diverse device desires, this study presented a web and device-attentive Quality of Service (QoS) approach that afford multimedia data applicable for a workstation unit surroundings via interactive mobile sustaining services, further considering the overall network environment and adapt the interactive transference frequency and the changing multimedia transcoding, to evade the waste of bandwidth and terminal power. Finally, this study realized a precursor of this architecture to verify the probability of the proposed method. According to the, analysis this method could give efficient versatile video sustaining services for varying bandwidth surroundings.

   Characteristics

   • Agility enhances with users' ability to re-provision technological infrastructure resources.

   • Application programming interface (API)

     accessibility to software that enables machines to communicate with cloud software in the same way the user interface facilitates interaction between humans and computers. Cloud computing systems typically use REST-based APIs.

     Virtualization

     Virtualization (or virtualisation) is the innovation of a virtual (rather than actual) version of something, such as a hardware platform, operating system (OS), storage device, or network resources. While a physical computer in the classical sense is complete and actual machine, both subjectively (from the user's view) and objectively (from the hardware system view), a virtual machine is subjectively a complete machine (or very close), but objectively merely a set of files and running programs on an actual, physical machine (which the user need not necessarily be aware of).

     Android

     It is a free, open source mobile platform. Linux- based, multi process, Multithreaded OS. Android is not a device or a product its not even limited to phones you could build a DVR, a handheld GPS, an MP3 player, etc. Android is a software stack for mobile devices that includes an operating system, middleware and key applications.

     Features

   • Application framework providing reuse and replacement of components.

   • Optimized graphics powered by a custom 2D graphics library; 3D graphics based on the OpenGL ES 1.0 specification.

   • SQLite is a transactional SQL database engine. Media support for multimedia services like common audio, video, and still image formats (MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, GIF).

     Android Runtime

   • Android includes a set of core libraries that delivers most of the functionality available in the core libraries of the Java programming language.

   • Every Android application runs in its own process and also with its own instance of the DalvikVM(virtual Machine). Dalvik has been written so that a device can run multiple VMs effectively and efficiently.

     Application Framework

   • Being an open development platform, Android offers developers the ability to build extremely rich and innovative applications.

   • Developers have full access to the same framework APIs used by the core applications.

2. LITERATURE SURVEY

   1. On Monitoring and Controlling QoS Network Domains

      Media cloud gives a cost-effective and capable solution for the coming stream of the media expenditure. Based on the modern work on media cloud investigation, we first form some suggestions on how to frame the media cloud, and then introduce some potentially promising topics for forthcoming research. In this, Edge routers monitor (using tomography techniques) a web domain to disclose quality of service (QoS) infraction possibly caused by under provisioning as well as bandwidth theft and denial of service (DoS) attacks. To bind the control overhead, the router only verifies service level agreement (SLA) parameters such as delay, loss, and throughput when deviation are disclose. Prior simulation solution indicate that this design boost the application-level throughput of data applications such as large FTP transfers.[1]

   2. Aggregated QoS Mapping Framework for Relative Service Differentiation-Aware Video Streaming.

      This article propose the principal approach of multimedia cloud computing and presents an innovative framework. We address multimedia cloud computing from multimedia-awake cloud (media cloud) and cloud-awake multimedia (cloud media) aspects. First, we present a multimedia-awake cloud, which addresses how a cloud can implement distributed multimedia processing and storage and refine quality of service (QoS) provisioning for multimedia services. To achieve a huge QoS for multimedia services, we offer a media-edge cloud (MEC) architecture, in which storage, central processing unit (CPU), and graphics processing unit (GPU) clusters are granted at the edge to provide distributed parallel processing and QoS variation for various types of devices.[2]

   3. Behaviour Signature for Fine-grained Traffic Identification

      We are planning to bring out an authorization and a complete experimental estimate of the performance of our cross-layer architecture as promptly as its advancement will be completed. In addition, we would like to enhance our study on this class of architectures to examined the brunt of dependability issues, such as fault tolerance and security, on their design.[3]

   4. Distributed Scheduling Scheme for Video Streaming over Multi-Channel Multi-Radio Multi-Hop Wireless Networks

      In this paper, we have matured fully distributed scheduling pattern that jointly determine the channel- assignment,rate allocation, routing and fairness problems for video sustaining over multi-channel multi-radio networks. Unlike conventional scheduling pattern focus on excellent system throughput or scheduling efficiency, our work ambition at accomplish minimal video misinterpretation and positive fairness by jointly considering media-awake distribution and network resource allocation. Extensive simulation solutions are provided which expose the effectiveness of our planned schemes.[4]

   5. Measurement, Modeling, and Analysis of a Peer-to-Peer File-Sharing Workload.

      In this paper, we introduce a clustering-based cloud node collection approach for communication-comprehensive cloud applications. By taking asset of the cluster analysis,

      our approach not only grant the QoS values of cloud nodes, but also examine the relationship (i.e., response time) between cloud nodes. Our approach consistently joins cluster analysis and ranking methods. The empirical results show that our approach outperforms the current ranking approaches.[5]

   6. From QoS to QoE: A Tutorial on Video Quality Assessment

      This paper proposed cross-layer playback-rate based streaming services, that can maintain network transmission quality and receive data before playback IMS networks reliably with many users. The experimental results show that the services could reduce the load in overall network without the occurrence of dropped packets.[6]

   7. A Novel IEEE 802.11aa Intra-AC Prioritization Method for Video Transmissions

      This paper focuses on the improvement of video transmission over WLANs using two video transmission queues (primary and alternate) and the two algorithms for traffic selection and the behavior of IEEE 802.11aa and legacy EDCA is compared. And also, it provides the possible ways for CBSA implementations. Then it proposes an adjustment of CBSA to wireless environment (WCBSA) which provides the most promising results[7].

   8. Improving Image Quality and Interactivity in Free- Viewpoint Video Transmission

   This paper proposes, three types of control for the depth and image transmission method in free-viewpoint video transmission. One is buffering control, another is prediction control, and the other is a combination of both types of control. The buffering control is used to buffer the viewpoint information, and the prediction control is used to predict the information. The effects of the three types of control by Quality of Experience (QoE) assessment was investigated. This assessment results show that the optimum buffering time exist in the buffering control and the optimum prediction time exist in the prediction control. Also, the buffering control improves the image quality, but the interactivity is degraded. The image quality becomes higher by the prediction control.[8]

3. EXISTING SYSTEM

In the existing system, the mobile device exchanges information with the cloud environment, in order to determine an optimum multimedia video. There are numerous researches made towards the conventional platform (CDN) to store different video formats in a multimedia server, to select the right video stream according to the current network situation or the hardware calculation capabilities. To solve this problem, many researchers have tried dynamic encoding for transferring video content, but yet cannot produce the best quality of video.

Limitations

• Video communication over mobile broadband networks is very challenging due to bandwidth limitations and difficulties in maintaining high reliability, quality, and latency demands imposed by rich multimedia applications.

• The use of multimedia content and applications leads to increase in network traffic.

  Proposed System:

  The proposed system provides an efficient interactive streaming service for different mobile devices and for dynamic network environments.

  When a mobile device requests a video streaming service, its hardware and network environment parameters will be transmitted to the profile agent in the cloud environment, which records the mobile device codes and determines the required parameters.

  Then it will be transmitted to the Network and Device-Aware Multi-layer Management (NDAMM). It determines the most suitable SVC code for the mobile device based on the parameters, and then the SVC Transcoding Controller (STC) gives the transcoding work via map-reduce to the cloud, so as to increase the transcoding rate.

  Then the multimedia video file will be transmitted to the mobile device through the service.

  Advantage:

  The network bandwidth can be changed dynamically.

  This method could provide efficient versatile multimedia streaming services.

  1. ARCHITECTURAL DIAGRAM

  2. UML Diagrams

  1. Use Case Diagram

  4. Activity Diagram

  Android Phone

  Application

  User

  Android Phone

  Application

  User

  GPRS Cloud Server

  Searching File

  Search Device

  Media File Configura…

  1. Class Diagram

  2. Collaboration Diagram

  Reterive File

  6: User Profile

  9: SVC Transcoding

  Media Profile Cloud Server Retrieve Media File Display

  5. Sequence Diagram

  User Application Phone Media Services

  Request

  Response

  Search Media data

  1: Request

  User 3: Search Media data

  Application

  7: Request Data Media Services

  Request

  2: Response 8: Media Data

  14: View Data

  Mobile Profile

  User Profile

  11: Maping 5: Mobile Profile 4: Request 13: SVC Data Formate

  10: Split Data

  Cloud 12: Result

  Phone

  NDAMM

  Request Data

  Media Data

  SVC Transcoding

  Split Data

  Maping

  Result

  VIII. MODULES

  1. User Account Module:

     The agent is used to receive the device hardware environment parameters and create a user profile. The mobile device transmits its hardware specifications in XML-schema format to the agent in the cloud server. The XML-schema is metadata, which is mainly semantic and used in describing the data format of the file. The metadata enables non-owner users to view information about the files, and its structure is protractile.

  2. Network and Device Alive Multi-Layer Management (NDAMM):

     The NDAMM aims to examine the interactive communication frequency and the SVC multimedia file coding parameters according to parameters of the device. It handover these to the STC for transcoding control, so as to minimize the communication bandwidth requirements and meet the mobile device users need for multimedia streaming.

     It consists of a listen module, a parameter profile module, a network evaluation module, a device-aware Bayesian prediction module, and adaptive multi-layer selection. The communicative multimedia streaming service must receive the user profile of the mobile device instantaneously through the listen module. The parameter profile module notice and store the user profile and determines the parameter. This is provided to both the network estimation module and the device-aware Bayesian prediction module to determine the required numerical values. Rw and Rh represent the width and height of the supportable resolution for the device, CP average and CP depicts the present and average CPU operating speed.

  3. Dynamitic Network Evaluation Module (DNEM):

     The DNEM is mainly based on the measurement- based prediction notion; however, it further develops the Exponentially Weighted Moving Average (EWMA). The EWMA uses the weights of the historical data and the present observed value to calculate gentle and flexible network bandwidth data for the dynamic adjustment of weights. In order to determine the precise network bandwidth value, the EWMA filter determines the network bandwidth value in which is the estimated bandwidth of the Number. t time interval, is the bandwidth of the Number. Time interval, and is the estimatio difference. For different mobile network evaluation, this study considered the error correction of estimation and the overall standard variation and estimated the different bandwidths by modifying the weights among which, is the moving average weight and is the standard deviation weight. When the prediction error is greater than, the system shall reduce the weight modification of the

     predicted difference comparatively, when the prediction error is less than, the system shall strengthen the weight modification of the predicted difference.

  4. Network and Device-Avail Bayesian Prediction Module (NDBPM):

     The SVC hierarchical structure contributes scalability of the temporal, spatial and quality dimensions. It modifies along with the FPS, resolution and video difference of a streaming bit rate: however, the question remains of how to choose a suitable video format according to the available resources of various devices. Hereby, in order to conform to the real-time requirements of mobile multimedia, this study acquired Bayesian theory to infer whether the video features conformed to the decoding action. The inference module was depends on the following two conditions:

• The LCD brightness does not always modify this hypothesis aims at a hardware energy evaluation. The literature depicts that TFT LCD energy consumption stands for about 20% 45% of total power consumption for different terminal hardware environments.

• The energy of the mobile device shall be sufficient for playing a full multimedia video. This Full multimedia service must be able to last till the user is satisfied. This assumed condition is also the next important decision rule.

1. Proposed Adaptive Communication and Multi-Layer Content Selection (ACMCS):

When the predicted bandwidth state and the Bayesian predictive network are predicted, the cloud system will further evaluate the communication and the required multimedia video files according to the information.

Communication Decision:

A good dynamic communication mechanism can minimize the bandwidth needs and the power consumption of the device resulting from extravagant packet transmission, and the transmission frequency can be determined according to the bandwidth and its fluctuation ratio depends on such dynamic decision-making. The transmit mode is engaged until the device finds a difference of the transmitted variables that exceeds a threshold. Although the threshold can minimize the communication frequency effectively and precisely, in this mode the mobile device must start up additional threads for continuous monitoring; thus, the load on the device side is increased.

SVC Multi-Layer Content Decision:

SVC is an improvement over traditional H.264/MPEG-4 AVC coding, as it has higher coding

pliability. It is categorized based on scalability as temporal, spatial and SNR, allowing multimedia service like video transmissions to be more adaptable to various heterogeneous network bandwidth. This study explored how to determine an appropriate multimedia video streaming service according to three major characteristics. Initially, the appropriate bandwidth interval was determined, in which the average bandwidth was used as the standard value and its each standard deviation was the bandwidth interval segment. A quadruple standard difference is assumed to be the boundary value. As communication and prediction mechanisms are constructed, the system will correct overall threshold according to the bandwidth variation automatically, in order to avoid the bandwidth boundary exceeding the practical situation.

REFERENCE:

1. Ahsan Habiby, Sonia Fahmy, Bharat Bhargava CERIAS and the Department of Computer Sciences Purdue University,

2. West Lafayette, IN 479071398 fhabib,fahmy, bbg@cs.purdue.edu

3. Jitae Shin, Jin-Gyeong Kim, JongWon Kim, D. C. Lee, and C.-C. Jay Kuo Integrated Media Systems Center and Department of Electrical Engineering-Systems University of Southern, E-mail: fjitaeshi,jingyeok, jongwon, cckuog@sipi.usc.edu, dclee@usc.edu

4. Mehdi Alasti and Behnam Neekzad, Clearwire\Jie Hui and Rath Vannithamby, Intel Labs

5. Sung-Ho Yoon, Jun-Sang Park and Myung-Sup Kim Dept. of Computer and Information Science, Korea University, Korea Received: 6 Jun. 2014, Revised: 5 Aug. 2014, Accepted: 7 Aug. 2014.

6. Krishna P. Gummadi, Richard J. Dunn, Stefan Saroiu, Steven

   D. Gribble, Henry M. Levy, and John ZahorjanChangsha,410073,P.R.China peifan@nudt.edu.cn,jiwang@ios.ac.cn

7. Yanjiao Chen, Student Member, IEEE, Kaishun Wu, Member,

   IEEE, and Qian Zhang, Fellow, IEEE

8. Katarzyna Kosek-Szott AGH University Krakow, Poland Email: kosek@kt.agh.edu.pl.

9. Marek Natkaniec AGH University Krakow, Poland Email: natkaniec@kt.agh.edu.pl Lukasz Prasnal AGH University Krakow, Poland Email: prasnal@kt.agh.edu.pl

10. Hiroya Sannomiya, Norishige Fukushima, and Yutaka Ishibashi Department of Scientific and Engineering

11. Simulation, Graduate School of Engineering, Nagoya Institute of Technology Nagoya 466-8555, Japan Email: sannomiya@mcl.nitech.ac.jp,{fukushima, ishibasi}@nitech.ac.jp.