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.
- Adobe HTTP Dynamic Streaming. http://www.adobe.com/products/hds-dynamic-streaming.html. Accessed: September, 25, 2014.Google Scholar
- Akamai Media Delivery Solution. http://www.akamai.com/mediadelivery. Accessed: October, 3, 2014.Google Scholar
- Akamai Transcoding. http://www.akamai.co.jp/enja/dl/brochures/sola_vision_transcoding_brief.pdf. Accessed: September, 25, 2014.Google Scholar
- Amazon Elastic Compute Cloud. http://aws.amazon.com/ec2/. Accessed: September, 25, 2014.Google Scholar
- Amazon Elastic Transcoder. http://aws.amazon.com/elastictranscoder/. Accessed: September, 25, 2014.Google Scholar
- Amazon Simple Storage Service. http://aws.amazon.com/s3/. Accessed: September, 25, 2014.Google Scholar
- Apple HTTP Live Streaming. https://developer.apple.com/resources/http-streaming/. Accessed: September, 25, 2014.Google Scholar
- Brightcove Zencoder. https://zencoder.com/en/. Accessed: September, 25, 2014.Google Scholar
- Encoder Cloud. http://www.encodercloud.com/. Accessed: September, 25, 2014.Google Scholar
- ExoGENI. http://wiki.exogeni.net. Accessed: September, 25, 2014.Google Scholar
- FFmpeg. https://www.ffmpeg.org/. Accessed: September, 25, 2014.Google Scholar
- 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 Scholar
- Microsoft Smooth Streaming. http://www.iis.net/downloads/microsoft/smooth-streaming. Accessed: September, 25, 2014.Google Scholar
- NetFlix Technical Details. http://en.wikipedia.org/wiki/Netflix#Internet_video_streaming. Accessed: September, 25, 2014.Google Scholar
- Rackspace Cloud Service. http://www.rackspace.com/. Accessed: September, 25, 2014.Google Scholar
- x264 Encoder. http://www.videolan.org/developers/x264.html. Accessed: September, 25, 2014.Google Scholar
- YouTube Video Formats. http://en.wikipedia.org/wiki/YouTube#Quality_and_codecs. Accessed: September, 25, 2014.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- S. S. Krishnan and R. K. Sitaraman. Video Stream Quality Impacts Viewer Behavior: Inferring Causality using Quasi-Experimental Designs. In IMC, November 2012. Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- R. Steinmetz and K. Nahrstedt. Multimedia Systems. X. media. publishing. Springer-Verlag, 2004. Google ScholarDigital Library
- T. Stockhammer. Dynamic Adaptive Streaming over HTTP --: Standards and Design Principles. In MMSys, February 2011. Google ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
Index Terms
Optimizing the video transcoding workflow in content delivery networks
Recommendations
SVC tunneling for media-aware content delivery: Impact on video quality
WOWMOM '11: Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia NetworksToday's omnipresent demand for access to multimedia content via diverse devices places new challenges on efficient content delivery. This work introduces the concept of Scalable Video Coding (SVC) tunneling developed in the EU FP7 ALICANTE project and ...
Multiview video transcoding: from multiple views to single view
PCS'09: Proceedings of the 27th conference on Picture Coding SymposiumAs multiview video is gaining more and more attentions, Multiview Video Coding (MVC) standard has been under development by the Joint Video Team as an extension to H.264/AVC. There will be increasingly more multiview video sources for both high end and ...
Leveraging the quantization offset for improved requantization transcoding of H.264/AVC video
PCS'09: Proceedings of the 27th conference on Picture Coding SymposiumRequantization transcoding is a method for reducing the bit rate of compressed video bitstreams. Most research on requantization is concerned with the architectural design, the selection of a suitable quantizer, or the reduction of requantization ...
Comments