Multimedia Systems

, Volume 22, Issue 5, pp 559–573 | Cite as

Optimal tree packing for discretized live rate-adaptive streaming in CDN

Regular Paper

Abstract

Rate-adaptive streaming technologies, such as the Dynamic Adaptive Streaming over HTTP (DASH) standard, provides an efficient and easy solution to stream multimedia in a heterogenous context. However, it reinforces the streaming capacity problem in the core Content Delivery Network (CDN) infrastructure since delivering one video means delivering an aggregation of multiple representations. In particular, for live rate-adaptive streaming, a large set of non-divisible data streams need to be either delivered in whole, or not delivered at all. Previous theoretical models that deal with streaming capacity problems are based on elastic bit rates, and do not capture these emerging features faced by today’s CDNs. In this paper, we identify a new, discretized streaming model, for live rate-adaptive video delivery in CDNs. For this model we formulate a general optimization problem and show that it is NP-complete. Then we study two fundamental scenarios that occur in real CDNs. For each of these scenarios, we present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large networks. These are the first sets of results for the discretized streaming model, and have both practical and theoretical importance in a topic that has become critical.

Keywords

Rate-adaptive streaming Discretized streaming model Optimization problem Algorithm 

References

  1. 1.
    Adler, M., Sitaraman, R.K., Venkataramani, H.: Algorithms for optimizing the bandwidth cost of content delivery. Comput Netw 55(18), 4007–4020 (2011)CrossRefGoogle Scholar
  2. 2.
    Akhshabi, S., Narayanaswamy, S., Begen, A.C., Dovrolis, C.: An experimental evaluation of rate-adaptive video players over HTTP. Signal Proc. Image Commun. 27(4), 271–287 (2012)CrossRefGoogle Scholar
  3. 3.
    Almeida, J.M., Eager, D.L., Vernon, M.K., Wright, S.J.: Minimizing delivery cost in scalable streaming content distribution systems. IEEE Trans. Multimed 6(2), 356–365 (2004)CrossRefGoogle Scholar
  4. 4.
    Andreev, K., Maggs, B., Meyerson, A., Saks, J., Sitaraman, R.: Algorithms for constructing overlay networks for live streaming. CoRR, 1109.4114 (2011)Google Scholar
  5. 5.
    Andreev, K., Maggs, B.M., Meyerson, A., Sitaraman, R.K.: Designing overlay multicast networks for streaming. In: Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures, SPAA'03, pp. 149–158 (2003)Google Scholar
  6. 6.
    Apple. Using http live streaming. http://goo.gl/fJIwC
  7. 7.
    Bertrand, G., Stephan, E., Burbridge, T., Eardley, P., Ma, K., Watson, G.: Use cases for content delivery network interconnection. RFC 6770 (2012)Google Scholar
  8. 8.
    Blum, C., Blesa, M.J.: New metaheuristic approaches for the edge-weighted k-cardinality tree problem. Comput. Oper. Res. 32, 1355–1377 (2005)CrossRefMATHGoogle Scholar
  9. 9.
    De Cicco, L., Mascolo, S., Palmisano, V.: Feedback control for adaptive live video streaming. In: Proceedings of the second annual ACM conference on Multimedia systems, MMSys '11, pp. 145–156 (2011)Google Scholar
  10. 10.
    Cisco. Visual Networking Index: Forecast and Methodology, 2014-2019. Technical report, Cisco Inc. (2014). http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white_paper_c11-481360.html/
  11. 11.
    Goemans, M.X.: Minimum bounded degree spanning trees. In: Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS '06, pp. 273–282 (2006)Google Scholar
  12. 12.
    Hosseini, M., Ahmed, D.T., Shirmohammadi, S., Georganas, N.D.: A survey of application-layer multicast protocols. IEEE Commun. Surveys Tutor. 9(3), 58–74 (2007)CrossRefGoogle Scholar
  13. 13.
    Ingram, M.: You think the internet is big now? akamai needs to grow 100-fold. Om Malik, Jun. 2012. http://gigaom.com/cloud/you-think-the-internet-is-big-now-akamai-needs-to-grow-100-fold/
  14. 14.
    Karp, R.M.: Reducibility among combinatorial problems. In: Complexity of Computer Computations, pp. 85–103 (1972)Google Scholar
  15. 15.
    Kim, J., Srikant, R.: Achieving the maximum p2p streaming rate using a small number of trees. In: Proceedings of 20th International Conference on Computer Communications and Networks, ICCCN '11, pp. 1–6 (2011)Google Scholar
  16. 16.
    Könemann, J., Ravi, R.: Primal-dual meets local search: approximating MSTs with nonuniform degree bounds. SIAM J. Comput. 34(3), 763–773 (2005)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Kontothanassis, L., Sitaraman, R., Wein, J., Hong, D., Kleinberg, R., Mancuso, B., Shaw, D., Stodolsky, D.: A transport layer for live streaming in a content delivery network. Proc. IEEE 92(9), 1408–1419 (2004)CrossRefGoogle Scholar
  18. 18.
    Krogfoss, B.: Analysis: content peering and the internet economy. Technical report, Alcatel Lucent (2011). http://www2.alcatel-lucent.com/techzine/analysis-content-peering-and-the-internet-economy/
  19. 19.
    Kurian, J., Sarac, K.: A survey on the design, applications, and enhancements of application-layer overlay networks. ACM Comput. Surv., 43(1), 5:1–5:34 (2010)Google Scholar
  20. 20.
    Le Faucheur, F.: CDN Federations: Lessons From Phase Two of the CDN Federation Pilot. In: CDN Summit (2012). http://www.contentdeliverysummit.com/2012
  21. 21.
    Liu, C., Bouazizi, I., Hannuksela, M.M., Gabbouj, M.: Rate adaptation for dynamic adaptive streaming over http in content distribution network. Signal Process. Image Commun. 27(4), 288–311 (2012)CrossRefGoogle Scholar
  22. 22.
    Liu, J., Rosenberg, C., Simon, G., Texier, G.: Optimal delivery of rate-adaptive streams in underprovisioned networks. IEEE J. Sel. Areas Commun. 32(4), 706–718 (2014)CrossRefGoogle Scholar
  23. 23.
    Liu, J., Simon, G.: Fast near-optimal algorithm for delivering multiple live video channels in cdns. In: Proceedings of 22th International Conference on Computer Communications and Networks, ICCCN '13, pp. 1–7 (2013)Google Scholar
  24. 24.
    Ni, J., Tsang, D.H.K.: Large-scale cooperative caching and application-level multicast in multimedia content delivery networks. IEEE Commun. Mag. 43(5), 98–105 (2005)CrossRefGoogle Scholar
  25. 25.
    Niu, D., Li, B.: Asymptotic optimality of randomized peer-to-peer broadcast with network coding. In: Proceedings of IEEE INFOCOM 2011, pp. 1197–1205 (2011)Google Scholar
  26. 26.
    Nygren, E., Sitaraman, R.K., Sun, J.: The Akamai network: a platform for high-performance internet applications. Oper. Syst. Rev. 44(3), 2–19 (2010)CrossRefGoogle Scholar
  27. 27.
    Passarella, A.: A survey on content-centric technologies for the current internet: Cdn and p2p solutions. Comput. Commun. 35(1), 1–32 (2012)CrossRefGoogle Scholar
  28. 28.
    Pires, K., Simon, G.: Dash in twitch: adaptive bitrate streaming in live game streaming platforms. In: Proceedings of the 2014 Workshop on Design, Quality and Deployment of Adaptive Video Streaming, VideoNext’14, pp. 13–18 (2014)Google Scholar
  29. 29.
    Pires, K., Simon, G.: Youtube live and twitch: a tour of user-generated live streaming systems. In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys ’15, pp. 225–230 (2015)Google Scholar
  30. 30.
    Reza, M.R., Bais, A., Sarshar, N.: On fair and optimal multi-source ip-multicast. Comput. Netw. 56(4), 1503–1524 (2012)CrossRefGoogle Scholar
  31. 31.
    Sengupta, S., Liu, S., Chen, M., Chiang, M., Li, J., Chou, P.A.: Peer-to-peer streaming capacity. IEEE Trans. Inf. Theory 57(8), 5072–5087 (2011)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Stockhammer, T.: Dynamic adaptive streaming over http: standards and design principles. In: Proceedings of the second annual ACM conference on Multimedia systems, MMSys '11, pp. 133–144 (2011)Google Scholar
  33. 33.
    Sweha, R., Ishakian, V., Bestavros, A.: AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction. In: Proceedings of the third annual ACM conference on Multimedia systems, MMSys '12, pp. 191–202 (2012)Google Scholar
  34. 34.
    Toni, L., Aparicio-Pardo, R., Simon, G., Blanc, A., Frossard, P.: Optimal set of video representations in adaptive streaming. In: Proceedings of the 5th ACM Multimedia Systems Conference, MMSys’14, pp. 271–282 (2014)Google Scholar
  35. 35.
    Zhao, C., Lin, X., Wu, C.: The streaming capacity of sparsely-connected P2P systems with distributed control. In: Proceedings of IEEE INFOCOM 2011, pp. 1449–1457 (2011)Google Scholar
  36. 36.
    Zhou, F., Ahmad, S., Buyukkaya, E., Simon, G., Hamzaoui, R.: Minimizing server throughput for low-delay live streaming in content delivery networks. In: Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV '12, pp. 65–70 (2012)Google Scholar
  37. 37.
    Zhuang, Z., Guo, C.: Optimizing cdn infrastructure for live streaming with constrained server chaining. In: Proceedings of the IEEE 9th International Symposium on Parallel and Distributed Processing with Applications, ISPA '11, pp. 183–188 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Xidian UniversityXi’anChina
  2. 2.Telecom BretagneRennesFrance

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