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Optimizing the video transcoding workflow in content delivery networks

Published:18 March 2015Publication History

ABSTRACT

The current approach to transcoding in adaptive bit rate streaming is to transcode all videos in all possible bit rates which wastes transcoding resources and storage space, since a large fraction of the transcoded video segments are never watched by users. To reduce transcoding work, we propose several online transcoding policies that transcode video segments in a "just-in-time" fashion such that a segment is transcoded only to those bit rates that are actually requested by the user. However, a reduction in the transcoding work should not come at the expense of a significant reduction in the quality of experience of the users. To establish the feasibility of online transcoding, we first show that the bit rate of the next video segment requested by a user can be predicted ahead of time with an accuracy of 99.7% using a Markov prediction model. This allows our online algorithms to complete transcoding the required segment ahead of when it is needed by the user, thus reducing the possibility of freezes in the video playback. To derive our results, we collect and analyze a large amount of request traces from one of the world's largest video CDNs consisting of over 200 thousand unique users watching 5 million videos over a period of three days. The main conclusion of our work is that online transcoding schemes can reduce transcoding resources by over 95% without a major impact on the users' quality of experience.

References

  1. Adobe HTTP Dynamic Streaming. http://www.adobe.com/products/hds-dynamic-streaming.html. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  2. Akamai Media Delivery Solution. http://www.akamai.com/mediadelivery. Accessed: October, 3, 2014.Google ScholarGoogle Scholar
  3. Akamai Transcoding. http://www.akamai.co.jp/enja/dl/brochures/sola_vision_transcoding_brief.pdf. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  4. Amazon Elastic Compute Cloud. http://aws.amazon.com/ec2/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  5. Amazon Elastic Transcoder. http://aws.amazon.com/elastictranscoder/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  6. Amazon Simple Storage Service. http://aws.amazon.com/s3/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  7. Apple HTTP Live Streaming. https://developer.apple.com/resources/http-streaming/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  8. Brightcove Zencoder. https://zencoder.com/en/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  9. Encoder Cloud. http://www.encodercloud.com/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  10. ExoGENI. http://wiki.exogeni.net. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  11. FFmpeg. https://www.ffmpeg.org/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  12. Global Internet Phenomena Report. https://www.sandvine.com/downloads/general/global-internet-phenomena/2014/1h-2014-global-internet-phenomena-report.pdf. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  13. Microsoft Smooth Streaming. http://www.iis.net/downloads/microsoft/smooth-streaming. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  14. NetFlix Technical Details. http://en.wikipedia.org/wiki/Netflix#Internet_video_streaming. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  15. Rackspace Cloud Service. http://www.rackspace.com/. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  16. x264 Encoder. http://www.videolan.org/developers/x264.html. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  17. YouTube Video Formats. http://en.wikipedia.org/wiki/YouTube#Quality_and_codecs. Accessed: September, 25, 2014.Google ScholarGoogle Scholar
  18. M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon. I Tube, You Tube, Everybody Tubes: Analyzing the World's Largest User Generated Content Video System. In IMC, October 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Finamore, M. Mellia, M. M. Munafò, R. Torres, and S. G. Rao. Youtube Everywhere: Impact of Device and Infrastructure Synergies on User Experience. In IMC, November 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. Kllapi, E. Sitaridi, M. M. Tsangaris, and Y. Ioannidis. Schedule Optimization for Data Processing Flows on the Cloud. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. S. Ko, S. Park, and H. Han. Design Analysis for Real-time Video Transcoding on Cloud Systems. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. S. Krishnan and R. K. Sitaraman. Video Stream Quality Impacts Viewer Behavior: Inferring Causality using Quasi-Experimental Designs. In IMC, November 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. K. Krishnappa, D. Bhat, and M. Zink. Dashing YouTube: An Analysis of Using DASH in YouTune Videa Service. In IEEE Proceedings of LCN, October 2013.Google ScholarGoogle Scholar
  24. P. Li, B. Veeravalli, and A. Kassim. Design and Implementation of Parallel Video Encoding Strategies using Divisible Load Analysis. Circuits and Systems for Video Technology, IEEE Transactions on, 15(9):1098--1112, Sept 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Z. Li, Y. Huang, G. Liu, F. Wang, Z.-L. Zhang, and Y. Dai. ICloud Transcoder: Bridging the Format and Resolution Gap between Internet Videos and Mobile Devices. In Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. H. Ma, B. Seo, and R. Zimmermann. Dynamic Scheduling on Video Transcoding for MPEG DASH in the Cloud Environment. In Proceedings of the 5th ACM Multimedia Systems Conference, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. Nygren, R. Sitaraman, and J. Sun. The Akamai Network: A platform for high-performance Internet applications. ACM SIGOPS Operating Systems Review, 44(3):2--19, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. I. Shin and K. Koh. Hybrid Transcoding for QoS Adaptive Video-on-Demand Services. Consumer Electronics, IEEE Transactions on, 50(2):732--736, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. J. A. Stankovic, M. Spuri, K. Ramamritham, and G. C. Buttazzo. Introduction. In Deadline Scheduling for Real-Time Systems, pages 1--11. Springer, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  30. R. Steinmetz and K. Nahrstedt. Multimedia Systems. X. media. publishing. Springer-Verlag, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. T. Stockhammer. Dynamic Adaptive Streaming over HTTP --: Standards and Design Principles. In MMSys, February 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Z. Wang, L. Sun, C. Wu, W. Zhu, and S. Yang. Joint Online Transcoding and Geo-distributed Delivery for Dynamic Adaptive Streaming. In INFOCOM, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  33. L. Xiaowei, C. Yi, and X. Yuan. Towards an Automatic Parameter-Tuning Framework for Cost Optimization on Video Encoding Cloud. International Journal of Digital Multimedia Broadcasting, 2012(935724):11, Sept 2012.Google ScholarGoogle Scholar
  34. M. Zink, K. Suh, Yu, and J. Kurose. Characteristics of YouTube Network Traffic at a Campus Network - Measurements, Models, and Implications. Elsevier Computer Networks, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          MMSys '15: Proceedings of the 6th ACM Multimedia Systems Conference
          March 2015
          277 pages
          ISBN:9781450333511
          DOI:10.1145/2713168

          Copyright © 2015 ACM

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          Publication History

          • Published: 18 March 2015

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          MMSys '15 Paper Acceptance Rate12of41submissions,29%Overall Acceptance Rate176of530submissions,33%

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