skip to main content
research-article

DQ-DASH: A Queuing Theory Approach to Distributed Adaptive Video Streaming

Published:04 March 2020Publication History
Skip Abstract Section

Abstract

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a Distributed Queuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a Mx/D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

References

  1. Vijay Kumar Adhikari, Yang Guo, Fang Hao, Matteo Varvello, Volker Hilt, Moritz Steiner, and Zhi-Li Zhang. 2012. Unreeling Netflix: Understanding and improving multi-CDN movie delivery. In IEEE INFOCOM. 1620--1628.Google ScholarGoogle Scholar
  2. Saamer Akhshabi, Ali C. Begen, and Constantine Dovrolis. 2011. An experimental evaluation of rate adaptation algorithms in adaptive streaming over HTTP. In ACM MMSys. 157--168.Google ScholarGoogle Scholar
  3. Abdelhak Bentaleb, Ali C. Begen, Saad Harous, and Roger Zimmermann. 2018. Want to play DASH?: A game theoretic approach for adaptive streaming over HTTP. In ACM MMSys. 13--26.Google ScholarGoogle Scholar
  4. Abdelhak Bentaleb, Ali C. Begen, and Roger Zimmermann. 2016. SDNDASH: Improving QoE of HTTP adaptive streaming using software defined networking. In ACM MM. 1296--1305.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Abdelhak Bentaleb, Ali C. Begen, Roger Zimmermann, and Saad Harous. 2017. SDNHAS: An SDN-enabled architecture to optimize QoE in HTTP adaptive streaming. IEEE TMM 19, 10 (2017), 2136--2151.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann. 2019. A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE COMST 21, 1 (2019), 562--585. DOI:https://doi.org/10.1109/COMST.2018.2862938Google ScholarGoogle Scholar
  7. Olivier Brun and Jean-Marie Garcia. 2000. Analytical solution of finite capacity M/D/1 queues. Journal of Applied Probability (2000), 1092--1098.Google ScholarGoogle Scholar
  8. Joachim Bruneau-Queyreix, Jordi Mongay Batalla, Mathias Lacaud, and Daniel Negru. 2018. PMS: A novel scale-adaptive and quality-adaptive hybrid P2P/multisource solution for live streaming. ACM TOMM 14, 2s, Article 35 (May 2018), 25 pages.Google ScholarGoogle Scholar
  9. Joachim Bruneau-Queyreix, Mathias Lacaud, and Daniel Negru. 2017. A multiple-source adaptive streaming solution enhancing consumer’s perceived quality. In IEEE CCNC. 580--581.Google ScholarGoogle Scholar
  10. Jin Cao, William S. Cleveland, Dong Lin, and Don X. Sun. 2003. Internet traffic tends toward Poisson and independent as the load increases. In Nonlinear Estimation and Classification. Springer, 83--109.Google ScholarGoogle Scholar
  11. Jianyu Cao and Weixin Xie. 2018. Joint arrival process of multiple independent batch Markovian arrival processes. Statistics 8 Probability Letters 133 (2018), 42--49.Google ScholarGoogle Scholar
  12. VNI Cisco. 2017. Cisco Visual Networking Index: Forecast and Methodology. 2016--2021.Google ScholarGoogle Scholar
  13. Xavier Corbillon, Ramon Aparicio-Pardo, Nicolas Kuhn, Géraldine Texier, and Gwendal Simon. 2016. Cross-layer Scheduler for Video Streaming over MPTCP. In ACM MMSys. 7.Google ScholarGoogle Scholar
  14. DASH Reference Player. 2017. Retrieved from https://github.com/Dash-Industry-Forum/dash.js.Google ScholarGoogle Scholar
  15. Alexander N. Dudin, Vladimir M. Vishnevsky, and Julia V. Sinjugina. 2016. Analysis of the BMAP/G/1 queue with gated service and adaptive vacations duration. Telecommunication Systems 61, 3 (2016), 403--415.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kristian Evensen, Dominik Kaspar, Carsten Griwodz, Pål Halvorsen, Audun F. Hansen, and Paal Engelstad. 2012. Using bandwidth aggregation to improve the performance of quality-adaptive streaming. Elsevier IM 27, 4 (2012), 312--328.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kristian Evensen, Tomas Kupka, Dominik Kaspar, Pål Halvorsen, and Carsten Griwodz. 2010. Quality-adaptive scheduling for live streaming over multiple access networks. In ACM NOSSDAV. 21--26.Google ScholarGoogle Scholar
  18. Jeroen Famaey, Steven Latré, Ray van Brandenburg, M. Oskar van Deventer, and Filip De Turck. 2013. On the impact of redirection on HTTP adaptive streaming services in federated CDNs. In Springer IFIP AIMS. 13--24.Google ScholarGoogle Scholar
  19. DASH Industry Forum. 2014. Guidelines for Implementation: DASH-AVC/264 Test Cases and Vectors. Retrieved from http://dashif.org/guidelines/.Google ScholarGoogle Scholar
  20. Aditya Ganjam, Faisal Siddiqui, Jibin Zhan, Xi Liu, Ion Stoica, Junchen Jiang, Vyas Sekar, and Hui Zhang. 2015. C3: Internet-scale control plane for video quality optimization. In NSDI. 131--144.Google ScholarGoogle Scholar
  21. Bo Han, Feng Qian, Lusheng Ji, and Vijay Gopalakrishnan. 2016. MP-DASH: Adaptive video streaming over preference-aware multipath. In ACM CoNEXT. 129--143.Google ScholarGoogle Scholar
  22. Xiaojun Hei, Chao Liang, Jian Liang, Yong Liu, and Keith Ross. 2006. Insight into PPLive: A measurement study of a large-scale P2P IPTV system. In WWW Workshop of IPTV Services.Google ScholarGoogle Scholar
  23. Professor Vector. 2017. Show that ∑ni=0 ((-1)i/i!)(n-i)ien-i=2n+⅔+o(1). Mathematics Stack Exchange. Retrieved April 16, 2017 from https://math.stackexchange.com/q/2236578.Google ScholarGoogle Scholar
  24. Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2015. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In ACM SIGCOMM. 187--198.Google ScholarGoogle Scholar
  25. ISO/IEC 23009-1:2014 Information technology—Dynamic adaptive streaming over HTTP (DASH). 2014. Retrieved from https://www.iso.org/standard/65274.html.Google ScholarGoogle Scholar
  26. Junchen Jiang, Vyas Sekar, and Hui Zhang. 2014. Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE. IEEE/ACM TON 22, 1 (2014), 326--340.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Junchen Jiang, Shijie Sun, Vyas Sekar, and Hui Zhang. 2017. Pytheas: Enabling data-driven quality of experience optimization using group-based exploration-exploitation. In NSDI. 3.Google ScholarGoogle Scholar
  28. Dag Johansen, Håvard Johansen, Tjalve Aarflot, Joseph Hurley, Åge Kvalnes, Cathal Gurrin, Sorin Zav, Bjørn Olstad, Erik Aaberg, Tore Endestad, et al. 2009. DAVVI: A prototype for the next generation multimedia entertainment platform. In ACM MM. 989--990.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Parikshit Juluri, Venkatesh Tamarapalli, and Deep Medhi. 2015. SARA: Segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. In IEEE ICCW. 1765--1770.Google ScholarGoogle Scholar
  30. Dominik Kaspar, Kristian Evensen, Paal Engelstad, Audun F. Hansen, Pål Halvorsen, and Carsten Griwodz. 2010. Enhancing video-on-demand playout over multiple heterogeneous access networks. In IEEE CCNC. 1--5.Google ScholarGoogle Scholar
  31. Juhoon Kim, Yung-Chih Chen, Ramin Khalili, Don Towsley, and Anja Feldmann. 2014. Multi-source multipath HTTP (mHTTP): A proposal. In ACM SIGMETRICS. 583--584.Google ScholarGoogle Scholar
  32. Jonathan Kua, Grenville Armitage, and Philip Branch. 2017. A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE COMST (2017).Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Robert Kuschnig, Ingo Kofler, and Hermann Hellwagner. 2010. Improving internet video streaming performance by parallel TCP-based request-response streams. In IEEE CCNC. 1--5.Google ScholarGoogle Scholar
  34. Stefan Lederer, Christopher Mueller, Christian Timmerer, Cyril Concolato, Jean Le Feuvre, and Karel Fliegel. 2013. Distributed DASH dataset. In ACM MMSys. 131--135.Google ScholarGoogle Scholar
  35. Stefan Lederer, Christopher Müller, and Christian Timmerer. 2012. Dynamic adaptive streaming over HTTP dataset. In ACM MMSys. 89--94.Google ScholarGoogle Scholar
  36. Zhi Li, Xiaoqing Zhu, Joshua Gahm, Rong Pan, Hao Hu, Ali C. Begen, and David Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE JSAC 32, 4 (2014), 719--733.Google ScholarGoogle ScholarCross RefCross Ref
  37. Chenghao Liu, Imed Bouazizi, and Moncef Gabbouj. 2011. Rate adaptation for adaptive HTTP streaming. In ACM MMSys. 169--174.Google ScholarGoogle Scholar
  38. Chenghao Liu, Imed Bouazizi, Miska M. Hannuksela, and Moncef Gabbouj. 2012. Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network. Elsevier IM 27, 4 (2012), 288--311.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Hongqiang Harry Liu, Ye Wang, Yang Richard Yang, Hao Wang, and Chen Tian. 2012. Optimizing cost and performance for content multihoming. In ACM SIGCOMM. 371--382.Google ScholarGoogle Scholar
  40. Yong Liu. 2007. On the minimum delay peer-to-peer video streaming: How realtime can it be? In ACM MM. 127--136.Google ScholarGoogle Scholar
  41. Zhengye Liu, Yanming Shen, Keith W. Ross, Shivendra S. Panwar, and Yao Wang. 2009. LayerP2P: Using layered video chunks in P2P live streaming. IEEE TMM 11, 7 (2009), 1340--1352.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In ACM SIGCOMM. 197--210.Google ScholarGoogle Scholar
  43. Matthew K. Mukerjee, Ilker Nadi Bozkurt, Devdeep Ray, Bruce M. Maggs, Srinivasan Seshan, and Hui Zhang. 2017. Redesigning CDN-Broker interactions for improved content delivery. In ACM CoNEXT. 68--80.Google ScholarGoogle Scholar
  44. Matthew K. Mukerjee, David Naylor, Junchen Jiang, Dongsu Han, Srinivasan Seshan, and Hui Zhang. 2015. Practical, real-time centralized control for CDN-based live video delivery. In ACM SIGCOMM. 311--324.Google ScholarGoogle Scholar
  45. Mehdi Nafaa and Nazim Agoulmine. 2009. Analysing joost peer to peer IPTV protocol. In IFIP/IEEE Integrated Network Management. IEEE, 291--294.Google ScholarGoogle Scholar
  46. Thinh P. Nguyen and Avideh Zakhor. 2001. Distributed video streaming over Internet. In Multimedia Computing and Networking, Vol. 4673. International Society for Optics and Photonics, 186--196.Google ScholarGoogle Scholar
  47. NUS. 2018. National Cybersecurity Laboratories (NCL) Testbed. Retrieved from https://ncl.sg.Google ScholarGoogle Scholar
  48. Stefano Petrangeli, Jeroen Famaey, Maxim Claeys, Steven Latré, and Filip De Turck. 2016. QoE-driven rate adaptation heuristic for fair adaptive video streaming. ACM TOMM 12, 2 (2016), 28.Google ScholarGoogle Scholar
  49. Wei Pu, Zixuan Zou, and Chang Wen Chen. 2011. Dynamic adaptive streaming over HTTP from multiple content distribution servers. In IEEE GLOBECOM. 1--5.Google ScholarGoogle Scholar
  50. Will Reese. 2008. Nginx: The high-performance web server and reverse proxy. Linux Journal 2008, 173 (2008), 2.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Sandvine. 2016. Video Quality of Experience: Requirements and Considerations for Meaningful Insight, white paper. [Online] https://www.sandvine.com/hubfs/downloads/archive/whitepaper-video-quality-of-experience.pdf.Google ScholarGoogle Scholar
  52. Kevin Spiteri, Rahul Urgaonkar, and Ramesh K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In IEEE INFOCOM. 1--9.Google ScholarGoogle Scholar
  53. Thomas Stockhammer. 2011. Dynamic adaptive streaming over HTTP--: Standards and design principles. In ACM MMSys. 133--144.Google ScholarGoogle Scholar
  54. Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In ACM SIGCOMM. 272--285.Google ScholarGoogle Scholar
  55. E. Thomas, M. O. van Deventer, T. Stockhammer, A. C. Begen, and J. Famaey. 2017. Enhancing MPEG DASH performance via server and network assistance. SMPTE Motion Imaging Journal 126, 1 (2017), 22--27.Google ScholarGoogle ScholarCross RefCross Ref
  56. Florian Wamser, Steffen Höfner, Michael Seufert, and Phuoc Tran-Gia. 2017. Server and content selection for MPEG DASH video streaming with client information. In ACM Internet-QoE. 19--24.Google ScholarGoogle Scholar
  57. Bing Wang, Wei Wei, Zheng Guo, and Don Towsley. 2009. Multipath live streaming via TCP: Scheme, performance and benefits. ACM TOMM 5, 3 (2009), 25.Google ScholarGoogle Scholar
  58. Praveen Kumar Yadav, Arash Shafiei, and Wei Tsang Ooi. 2017. QUETRA: A queuing theory approach to DASH rate adaptation. In ACM MM. 1130--1138.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In ACM SIGCOMM. 325--338.Google ScholarGoogle Scholar
  60. YouTube. 2018. YouTube Live Encoder Settings, Bitrates, and Resolutions. Retrieved from https://support.google.com/youtube/answer/2853702.Google ScholarGoogle Scholar
  61. Li Yu, Tammam Tillo, Jimin Xiao, and Marco Grangetto. 2017. Convolutional neural network for intermediate view enhancement in multiview streaming. IEEE Transactions on Multimedia 20, 1 (2017), 15--28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Hui Yuan, Huayong Fu, Ju Liu, Junhui Hou, and Sam Kwong. 2018. Non-cooperative game theory based rate adaptation for dynamic video streaming over HTTP. IEEE TMC 17, 10 (2018), 2334--2348.Google ScholarGoogle Scholar
  63. Hui Yuan, Huayong Fu, Ju Liu, and Jimin Xiao. 2016. End-to-end distortion-based multiuser bandwidth allocation for real-time video transmission over LTE network. IEEE Transactions on Broadcasting 63, 2 (2016), 338--349.Google ScholarGoogle ScholarCross RefCross Ref
  64. Hui Yuan, Xuekai Wei, Fuzheng Yang, Jimin Xiao, and Sam Kwong. 2018. Cooperative bargaining game-based multiuser bandwidth allocation for dynamic adaptive streaming over HTTP. IEEE TMM 20, 1 (2018), 183--197.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Xinyan Zhang, Jiangchuan Liu, Bo Li, and Y.-S. P. Yum. 2005. CoolStreaming/DONet: A data-driven overlay network for peer-to-peer live media streaming. In IEEE INFOCOM, Vol. 3. 2102--2111.Google ScholarGoogle Scholar
  66. Xue Zhang, Laura Toni, Pascal Frossard, Yao Zhao, and Chunyu Lin. 2018. Adaptive streaming in interactive multiview video systems. IEEE Transactions on Circuits and Systems for Video Technology 29, 4 (2018), 1130--1144.Google ScholarGoogle ScholarCross RefCross Ref
  67. Chao Zhou, Chia-Wen Lin, Xinggong Zhang, and Zongming Guo. 2014. A control-theoretic approach to rate adaption for DASH over multiple content distribution servers. IEEE TCSVT 24, 4 (2014), 681--694.Google ScholarGoogle Scholar
  68. Yipeng Zhou, Dah Ming Chiu, and John C. S. Lui. 2007. A simple model for analyzing P2P streaming protocols. In IEEE ICNP. 226--235.Google ScholarGoogle Scholar

Index Terms

  1. DQ-DASH: A Queuing Theory Approach to Distributed Adaptive Video Streaming

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 1
      February 2020
      363 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3384216
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 March 2020
      • Revised: 1 October 2019
      • Accepted: 1 October 2019
      • Received: 1 November 2018
      Published in tomm Volume 16, Issue 1

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!