Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud

  • Hyun-Woo Kim
  • He Mu
  • Jong Hyuk ParkEmail author
  • Arun Kumar Sangaiah
  • Young-Sik JeongEmail author
Original Research


Recently, intra-cloud research has been actively conducted to reduce the waste of idle resources in distributed desktops and to increase resource utilization. Intra-cloud integrates the idle resources of distributed desktops to provide computing and storage services to users. Existing intra-cloud have only studied storage of large files and simple computing services. Research is needed for computing services of multimedia field such as video and audio in the intra-cloud. This paper proposes a diversify scheme for multiform video resources (DSMVR), which is a video transcoding scheme of multimedia data-hiding based on the parallel computing framework and the intra-cloud environment, in order to transcode for multiform resource types within the intra-cloud, which composed to computing infrastructure using legacy desktops. Its target users are community user groups within a certain size. By using a small-scale server group, parallel processing framework and improved task assignment algorithm, high-speed video transcoding can be realized by using ffmpeg, which is a vast software suite of libraries and programs designed for handling video, audio, and other multimedia files and streams, and different-definition videos are generated step by step at high speed. By using the DSMVR scheme, the size of a task can be dynamically analyzed in order to select the number of task processing servers required, thus ensuring the high scalability of the DSMVR. Thanks to these operations, the user can smoothly play videos at resolutions that are suitable for different smart devices.


Video transcoding Distributed video transcoding Cloud video transcoding Video transcoding of multimedia data-hiding 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1A09000631).


  1. Ahmad I, Wei X, Sun Y, Zhang YQ (2005) Video transcoding: an overview of various techniques and research issues. IEEE Trans Multimed 7(5):793–804CrossRefGoogle Scholar
  2. Anderson TE (1990) The performance of spin lock alternatives for shared-memory multiprocessors. IEEE Trans Parallel Distrib Syst 1(1):6–16CrossRefGoogle Scholar
  3. Ashraf A (2013) Cost-efficient virtual machine provisioning for multi-tier web applications and video transcoding. In: Proceedings of the 13th IEEE/ACM international symposium on cluster, cloud and grid Comput, IEEE, Delft, Netherlands, 13–16 May, pp 66–69Google Scholar
  4. Fahim Y, Rahhali H, Hanine M, Benlahmar EH, Labriji EH, Hanoune M, Eddaoui A (2018) Load balancing in cloud computing using meta-heuristic algorithm. J Inf Process Syst 14:569–587Google Scholar
  5. Gao G, Hu H, Wen Y, Westphal C (2017) Resource provisioning and profit maximization for transcoding in clouds: a two-timescale approach. IEEE Trans Multimed 19(4):836–848CrossRefGoogle Scholar
  6. Gao G, Zhang W, Wang YWZ, Zhu W, Tan YP (2014) Cost optimal video transcoding in media cloud: insights from user viewing pattern. In: Proceedings of the IEEE international conference on multimedia and expo, IEEE, Chengdu, China, 14–18 Jul, pp 1–6Google Scholar
  7. Hsu TH, Wang ZY (2017) A distributed SHVC video transcoding system. In: Proceedings of the 10th international conference on Ubi-media computing and workshops, IEEE, Pattaya, Thailand, 1–4 AugGoogle Scholar
  8. Jokhio F, Ashraf A, Lafond S, Porres I, Lilius J (2013) Prediction-based dynamic resource allocation for video transcoding in cloud computing. In: Proceedings of 21st Euromicro international conference on parallel, distributed, and network-based processing, IEEE, Belfast, UK, 27 Feb.–1 Mar. 2013, pp 254-261Google Scholar
  9. Kim M, Cui Y, Han S, Lee H (2013) Towards efficient design and implementation of a hadoop-based distributed video transcoding system in cloud computing environment. Int J Multimed Ubiquitous Eng 8(2):213–224Google Scholar
  10. Maity S, Park JH (2016) Powering IoT devices: a novel design and analysis technique. J Converg 7:1–18Google Scholar
  11. Mesbahi MR, Rahmani AM, Hosseinzadeh M (2018) Reliability and high availability in cloud computing environments: a reference roadmap. Hum Centric Comput Inf Sci 18(20):1–31Google Scholar
  12. Shanableh T, Peixoto E, Izquierdo E (2013) MPEG-2 to HEVC video transcoding with content-based modeling. IEEE Trans Circ Syst Video Technol 23(7):1191–1196CrossRefGoogle Scholar
  13. Son S, Kim M (2017) HVTS: Haddop-based video transcoding system for media services. IEICE Trans Fund Electron Commun Comput Sci E100-A(5):1248–1253Google Scholar
  14. Vetro A, Christopoulos C, Sun H (2003) Video transcoding architectures and techniques: an overview. IEEE Signal Process Mag 20(2):18–29CrossRefGoogle Scholar
  15. Wu Y, Zhang Z, Wu C, Li Z, Lau FCM (2013) CloudMoV: Cloud-based mobile social TV. IEEE Trans Multimed 15(4):821–832CrossRefGoogle Scholar
  16. Zhou Y, Yu FR, Chen J, Kuo Y (2018) Video transcoding, caching, and multicast for heterogeneous networks over wireless network virtualization. IEEE Commun Lett 22(1):141–144CrossRefGoogle Scholar
  17. Zinner T, Hohlfeld O, Abboud O, Hossfeld T (2010) Impact of frame rate and resolution on objective QoE metrics. In: Proceedings of the second international workshop on quality of multimedia experience, IEEE, Trondheim, Norway, 21–23 Jun, pp 29–34Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Multimedia EngineeringDongguk UniversitySeoulKorea
  2. 2.Department of Computer Science and EngineeringSeoul National University of Science and TechnologySeoulKorea
  3. 3.School of Computing Science and EngineeringVellore Institute of Technology (VIT)VelloreIndia

Personalised recommendations