skip to main content
research-article
Public Access

ASAP: Adaptive Stall-Aware Pacing for Improved DASH Video Experience in Cellular Networks

Published:27 June 2018Publication History
Skip Abstract Section

Abstract

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.

References

  1. S. Akhshabi, L. Anantakrishnan, A. C. Begen, and C. Dovrolis. 2012. What happens when HTTP adaptive streaming players compete for bandwidth? In Proceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV’12). 9--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Akhshabi, L. Anantakrishnan, C. Dovrolis, and A. C. Begen. 2013. Server-based traffic shaping for stabilizing oscillating adaptive streaming players. In Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’13). 19--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Bouten, R. de O. Schmidt, J. Famaey, S. Latré, A. Pras, and F. De Turck. 2015. QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements. Comput. Netw. 81 (2015), 96--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Chen, R. Mahindra, M. A. Khojastepour, S. Rangarajan, and M. Chiang. 2013. A scheduling framework for adaptive video delivery over cellular networks. In Proceedings of the 19th Annual International Conference on Mobile Computing 8 Networking (MobiCom’13). 389--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Cofano, L. De Cicco, T. Zinner, A. Nguyen-Ngoc, P. Tran-Gia, and S. Mascolo. 2016. Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming. In Proceedings of the 7th International Conference on Multimedia Systems (MMSys’16). Article 3, 12 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L. De Cicco, V. Caldaralo, V. Palmisano, and S. Mascolo. 2013. ELASTIC: A client-side controller for dynamic adaptive streaming over HTTP (DASH). In Proceedings of the 20th International Packet Video Workshop (PV’13). 1--8.Google ScholarGoogle Scholar
  7. L. De Cicco and S. Mascolo. 2014. An adaptive video streaming control system: Modeling, validation, and performance evaluation. IEEE/ACM Trans. Netw. 22, 2 (Apr. 2014), 526--539. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, Gang Li, and R. Miller. 2013. Optimization of HTTP adaptive streaming over mobile cellular networks. In Proceedings of the 2013 IEEE International Conference on Computer Communications (INFOCOM’13). 898--997.Google ScholarGoogle Scholar
  9. V. Erceg, L. J. Greenstein, S. Y. Tjandra, S. R. Parkoff, A. Gupta, B. Kulic, A. A. Julius, and R. Bianchi. 1999. An empirically based path loss model for wireless channels in suburban environments. IEEE J. Select. Areas Commun. 17, 7 (Jul. 1999), 1205--1211. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. A. Galperin and Z. Waksman. 1981. A separable integer programming problem equivalent to its continual version. J. Comput. Appl. Math. 7, 3 (1981), 173--179.Google ScholarGoogle ScholarCross RefCross Ref
  11. P. Georgopoulos, Y. Elkhatib, M. Broadbent, M. Mu, and N. Race. 2013. Towards network-wide QoE fairness using openflow-assisted adaptive video streaming. In Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-centric Multimedia Networking (FhMN’13). 15--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Ghobadi, Y. Cheng, A. Jain, and M. Mathis. 2012. Trickle: Rate limiting youtube video streaming. In Proceedings of the 2012 USENIX Conference on Annual Technical Conference (USENIX ATC’12). 191--196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. S. Hochbaum and J. George Shanthikumar. 1990. Convex separable optimization is not much harder than linear optimization. J. ACM 37, 4 (Oct. 1990), 843--862. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. R. Houdaille and S. Gouache. 2012. Shaping HTTP adaptive streams for a better user experience. In Proceedings of the 3rd Multimedia Systems Conference (MMSys’12). 1--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Huang, R. Johari, N. McKeown, M. Trunnell, and M. Watson. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of the 2014 ACM Conference on SIGCOMM (SIGCOMM’14). 187--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Jiang, V. Sekar, and H. Zhang. 2012. Improving fairness, efficiency, and stability in http-based adaptive video streaming with FESTIVE. In Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies (CoNEXT’12). 97--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Jan Willem Kleinrouweler, Sergio Cabrero, and Pablo Cesar. 2017. An SDN architecture for privacy-friendly network-assisted DASH. ACM Trans. Multimedia Comput. Commun. Appl. 13, 3s, Article 44 (Jun. 2017), 22 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jan Willem Kleinrouweler, Britta Meixner, and Pablo Cesar. 2017. Improving video quality in crowded networks using a DANE. In Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’17). ACM, New York, NY, 73--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Kua, G. Armitage, and P. Branch. 2017. A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun. Surv. Tutor. 19, 3 (Jul. 2017), 1842--1866.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE J. Select. Areas Commun. 32, 4 (2014), 719--733.Google ScholarGoogle ScholarCross RefCross Ref
  21. R. K. P. Mok, E. W. W. Chan, and R. K. C. Chang. 2011. Measuring the quality of experience of HTTP video streaming. In Proceedings of the 2011 IFIP/IEEE International Symposium on Integrated Network Management (IM’11). 485--492.Google ScholarGoogle ScholarCross RefCross Ref
  22. R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. 2012. QDASH: A QoE-aware DASH system. In Proceedings of the 3rd Multimedia Systems Conference (MMSys’12). 11--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Mu, M. Broadbent, A. Farshad, N. Hart, D. Hutchison, Q. Ni, and N. Race. 2016. A scalable user fairness model for adaptive video streaming over SDN-assisted future networks. IEEE J. Select. Areas Commun. 34, 8 (Aug. 2016), 2168--2184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. M. Nguyen, L. B. Tran, H. T. Le, N. P. Ngoc, and T. C. Thang. 2015. An evaluation of segment duration effects in HTTP adaptive streaming over mobile networks. In Proceedings of the 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS’15). 248--253.Google ScholarGoogle Scholar
  25. S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. De Turck. 2015. QoE-driven rate adaptation heuristic for fair adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 12, 2, Article 28 (Oct. 2015), 24 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S. Petrangeli, J. Famaey, M. Claeys, S. Latré, and F. De Turck. 2015. QoE-driven rate adaptation heuristic for fair adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 12, 2, Article 28 (Oct. 2015), 24 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M.H. Pinson and S. Wolf. 2004. A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50, 3 (Sept. 2004), 312--322.Google ScholarGoogle ScholarCross RefCross Ref
  28. W. Pu, Z. Zou, and C. W. Chen. 2012. Video adaptation proxy for wireless dynamic adaptive streaming over HTTP. In Proceedings of the 2012 19th International Packet Video Workshop (PV’12). 65--70.Google ScholarGoogle Scholar
  29. J. J. Quinlan, D. Raca, A. H. Zahran, A. Khalid, K. K. Ramakrishnan, and C. J. Sreenan. 2016. D-LiTE: A platform for evaluating DASH performance over a simulated LTE network. In Proceedings of the 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN’16). 1--2.Google ScholarGoogle Scholar
  30. J. J. Quinlan, A. H. Zahran, and C. J. Sreenan. 2016. Datasets for AVC (H.264) and HEVC (H.265) for evaluating dynamic adaptive streaming over HTTP (DASH). In Proceedings of the ACM Multimedia Systems Conference (MMsys’16). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. A. Seetharam, P. Dutta, V. Arya, J. Kurose, M. Chetlur, and S. Kalyanaraman. 2015. On managing quality of experience of multiple video streams in wireless networks. IEEE Trans. Mobile Comput. 14, 3 (Mar. 2015), 619--631.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. T. C. Thang, H. T. Le, H. X. Nguyen, A. T. Pham, J. W. Kang, and Y. M. Ro. 2013. Adaptive video streaming over HTTP with dynamic resource estimation. J. Commun. Netw. 15, 6 (Dec. 2013), 635--644.Google ScholarGoogle ScholarCross RefCross Ref
  33. J. De Vriendt, D. De Vleeschauwer, and D. Robinson. 2013. Model for estimating QoE of video delivered using HTTP adaptive streaming. In Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM’13). 1288--1293.Google ScholarGoogle Scholar
  34. Jun Yao, Salil S. Kanhere, Imran Hossain, and Mahbub Hassan. 2011. Empirical evaluation of HTTP adaptive streaming under vehicular mobility. In NETWORKING 2011, Jordi Domingo-Pascual, Pietro Manzoni, Sergio Palazzo, Ana Pont, and Caterina Scoglio (Eds.). Springer, Berlin, 92--105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. F. Z. Yousaf, M. Liebsch, A. Maeder, and S. Schmid. 2013. Mobile CDN enhancements for QoE-improved content delivery in mobile operator networks. IEEE Netw. 27, 2 (Mar. 2013), 14--21.Google ScholarGoogle ScholarCross RefCross Ref
  36. Ahmed H. Zahran, Jason J. Quinlan, K. K. Ramakrishnan, and Cormac J. Sreenan. 2017. SAP: Stall-aware pacing for improved DASH video experience in cellular networks. In Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys’17). ACM, New York, NY, 13--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. A. H. Zahran and C. J. Sreenan. 2016. ARBITER: Adaptive rate-based intelligent HTTP streaming algorithm. In Proceedings of the 2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW’16). 1--6.Google ScholarGoogle Scholar

Index Terms

  1. ASAP: Adaptive Stall-Aware Pacing for Improved DASH Video Experience in Cellular Networks

        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 14, Issue 3s
          Special Section on Delay-Sensitive Video Computing in the Cloud and Special Section on Extended MMSys-NOSSDAV Best Papers
          June 2018
          317 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3233173
          Issue’s Table of Contents

          Copyright © 2018 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 27 June 2018
          • Accepted: 1 April 2018
          • Revised: 1 March 2018
          • Received: 1 September 2017
          Published in tomm Volume 14, Issue 3s

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed
        • Article Metrics

          • Downloads (Last 12 months)31
          • Downloads (Last 6 weeks)1

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader
        About Cookies On This Site

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

        Learn more

        Got it!