Abstract
Video content providers like YouTube and Netflix cater their content, i.e., news and shows, on the web which is accessible anytime anywhere. The multi-screens like TVs, smartphones, and laptops created a demand to transcode the video into the appropriate video specification ensuring different quality of services (QoS) such as delay. Transcoding a large, high-definition video requires a lot of time, computation. The cloud transcoding solution allows video service providers to overcome the above difficulties through the pay-as-you-use scheme, with the assurance of providing online support to handle unpredictable demands. This paper presents a cost-efficient cloud-based transcoding framework and algorithm (CVS) for streaming service providers. The dynamic resource provisioning policy used in framework finds the number of virtual machines required for a particular set of video streams. Simulation results based on YouTube dataset show that the CVS algorithm performs better compared to FCFS scheme.
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Sahoo, S., Parida, I., Mishra, S.K., Sahoo, B., Turuk, A.K. (2019). Resource Allocation for Video Transcoding in the Multimedia Cloud. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_55
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DOI: https://doi.org/10.1007/978-981-10-8639-7_55
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