Cloud video as a Service [VaaS] with storage, streaming, security and Quality of service: Approaches and directions

Kesavaraja, D.; Shenbagavalli, A.

doi:10.1109/iccpct.2013.6528975

Cited by 6 publications

(3 citation statements)

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“…Kesavaraja [12] apresenta um conceito que está crescendo atualmente, que é o de oferecer serviços de videoconferência na nuvem (VaaS -Video as a Service).…”

Section: Trabalhos Relacionadosunclassified

MCU em software distribuído em nuvem

Roesler

Júnior

Hüsemann

et al. 2018

Anais Estendidos Do XXIV Simpósio Brasileiro De Sistemas Multimídia E Web

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“…Kesavaraja [12] apresenta um conceito que está crescendo atualmente, que é o de oferecer serviços de videoconferência na nuvem (VaaS -Video as a Service).…”

Section: Trabalhos Relacionadosunclassified

MCU em software distribuído em nuvem

Roesler

Júnior

Hüsemann

et al. 2018

Anais Estendidos Do XXIV Simpósio Brasileiro De Sistemas Multimídia E Web

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“…The resources can be used in various metered way of access like hour based or day based and also is scalable as per the requirements of the user[l]. The user need not worry about fine tuning the resource since that responsibility will be taken by the resource provider, thereby just making the user utilize the service provided [7] [8][9]. This kind of service is called as Infrastructure as a service(IaaS).…”

Section: Dra Shenbagavallimentioning

confidence: 99%

Hadoop scalable Video Transcoding technique in cloud environment

Kesavaraja¹,

Shenbagavalli²

2015

2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

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Nowadays video is being produced and consumed in more component representation formats, more device types and over a variety of networks than ever. Transmission of video through network takes more time. So Video Transcoding is a very important factor when the video is moved between various heterogeneous clients in the cloud environment. Transcoding is a process of translating one coded form of video into another. However most of the time transcoding becomes computationally intensive and time consuming process. This proposed cloud system has four of the video compression standards such as Low Quality Encoding , Standard Quality Encoding , High Quality Encoding and High Definition. These standards achieve better compression performance with Quality. In general compression process takes more time. Map Reduce is used for managing a work in a considerable short period of time. This in turn helps in faster and efficient video transcoding. These Four standards are embedded into the Hadoop Distributed File System implementation and trial runs were done. Using the HDFS Map Reduce functionality, the video is splited using 64 MB blocks (Segments of Streams) and processed separately for maintaining efficiency in a time based aspect. Index Terms-Cloud Computing, Video Encoding, Hadoop, Map Reduce, FFMPEG 978-1-4799-6480-2/15/$31.00

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“…Video streaming is a popular multimedia application that allows users to watch video content on-demand over the internet Nikas et al (2018). The quality of video streaming depends on various factors, including the storage capacity, streaming protocols, and security measures used Kesavaraja and Shenbagavalli (2013). Afzal et al "In order to meet the increasing demand for video streaming, there is a need for efficient and reliable methods for delivering high-quality video to users Afzal et al (2019).…”

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confidence: 99%

Optimizing the Video Streaming Performance with an Energy-Efficient Evolution of Cub to Predator Based Resource Allocation

et al. 2024

Preprint

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Wireless sensor networks (WSNs) and video streaming services have gained popularity in recent years. The MPEG-4 H.264/AVC protocol has been widely adopted for video streaming in WSNs, but it is important to transmit sensitive video packets securely to protect the confidentiality of mission-critical applications. It is crucial to ensure that security measures do not negatively impact network performance or video quality. While previous research has focused on secure and energy-efficient video streaming over WSNs, none have addressed the integration of video quality optimization and energy consumption.This study aims to create a more efficient method of encrypting video streaming in wireless sensor networks (WSNs) by combining selective Elliptic Curve Cryptography (ECC) with Evolution of Cub to Predator (ECP). The proposed method improves energy, distortion, and encryption performance by up to 10 dB more than other methods (EEP, UEP (GA), and MCS-RA) through the use of network resource allocation with ECP-RA in WSNs. Quantitative measures such as MSE, PSNR, RMSE, and SSIM demonstrate that this approach is superior to others.

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Cloud video as a Service [VaaS] with storage, streaming, security and Quality of service: Approaches and directions

Cited by 6 publications

References 40 publications

MCU em software distribuído em nuvem

MCU em software distribuído em nuvem

Hadoop scalable Video Transcoding technique in cloud environment

Optimizing the Video Streaming Performance with an Energy-Efficient Evolution of Cub to Predator Based Resource Allocation

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