Multimedia Tools and Applications

, Volume 73, Issue 3, pp 1417–1443 | Cite as

A scalable delivery solution and a pricing model for commercial video-on-demand systems with video advertisements

  • Musab S. Al-HadrusiEmail author
  • Nabil J. Sarhan


This paper presents a scalable delivery solution for commercial near on-demand video streaming systems with an associated pricing model. The proposed delivery solution combines the benefits of periodic broadcasting and stream merging, thereby enabling scalable video delivery. Video advertisements are delivered to the clients prior to viewing the requested videos. The revenues generated from the ads are used to subsidize the price of the requested videos. The pricing is determined based on the total ad viewing time. The proposed solution includes an efficient ad allocation scheme and a new constraint-based scheduling approach. In addition, the paper investigates how targeted advertisements can be efficiently supported. Furthermore, we investigate the effectiveness of the overall solutions and analyze and compare the effectiveness of various scheduling policies and ad allocation alternatives in terms of several metrics, including client defection probability, average number of viewed ads per client, price, channel utilization, revenue, and profit.


Periodic broadcasting Pricing Scheduling Stream merging Supporting advertisements Targeted advertisements Video streaming 


  1. 1.
    Aggarwal V, Caldebank R, Gopalakrishnan V, Jana R, Ramakrishnan K, Yu F (2009) The effectiveness of intelligent scheduling for multicast video-on-demand. In: Proc. of ACM multimedia, pp 421–430Google Scholar
  2. 2.
    Al-Hadrusi M, Sarhan NJ (2008) A scalable delivery framework and a pricing model for streaming media with advertisements. In: Proc. of SPIE/ACM multimedia computing and networking conference (MMCN), pp 68,180G–68,180GGoogle Scholar
  3. 3.
    Al-Hadrusi MS, Sarhan NJ (2012) Client-driven price selection for scalable video streaming with advertisements. In: Proc. of IEEE the international multimedia modeling conference (MMM)Google Scholar
  4. 4.
    Basu P, Little TDC (2000) Pricing considerations in video-ondemand systems. In: Proc. of ACM multimedia, pp 359–361Google Scholar
  5. 5.
    Basu P, Narayanan A, Ke W, Little TDC, Bestavros A (1999) Optimal scheduling of secondary content for aggregation in video-ondemand systems. In: Proc. of international conference on computer communications and networks, pp 104–109Google Scholar
  6. 6.
    Bruner RE, Singh J (2007) Video ad benchmarks: average campaign performance metrics. A double click rich media and video report. Available from:
  7. 7.
    Cai Y, Hua KA (1999) An efficient bandwidth-sharing technique for true video on demand systems. In: Proc. of ACM multimedia, pp 211–214Google Scholar
  8. 8.
    Carlsson N, Eager DL, Vernon MK (2006) Multicast protocols for scalable on-demand download. Perform Eval 63(9–10):864–891CrossRefGoogle Scholar
  9. 9.
    Cheng X, Dale C, Liu J (2008) Statistics and social network of YouTube videos. In: Proc. of IEEE 16th international workshop on quality of service (IWQoS)Google Scholar
  10. 10.
    Chien WD, Yeh YS, Wang JS, Wang JS (2004) Practical channel transition for near-VOD services. In: IEEE international conference on multimedia and expo (ICME), pp 1843–1846Google Scholar
  11. 11.
    Dan A, Sitaram D, Shahabuddin P (1994) Scheduling policies for an on-demand video server with batching. In: Proc. of ACM multimedia, pp 391–398Google Scholar
  12. 12.
    Deering S, Fenner W, Haberman B, Haberman B (1999) Multicast listener discovery (MLD) for IPv6. In: Internet engineering task force (IETF), RFC2710Google Scholar
  13. 13.
    Eager DL, Vernon MK, Zahorjan J (1999) Optimal and efficient merging schedules for Video-on-Demand servers. In: Proc. of ACM multimedia, pp 199–202Google Scholar
  14. 14.
    Eager DL, Vernon MK, Zahorjan J (2001) Minimizing bandwidth requirements for on-demand data delivery. IEEE Trans Knowl Data Eng 13(5):742–757CrossRefGoogle Scholar
  15. 15.
    Gao L, Kurose J, Towsley D (1998) Efficient schemes for broadcasting popular videos. In: Proc. of the int’l workshop on network and operating systems support for digital audio and video (NOSSDAV)Google Scholar
  16. 16.
    Ge Z, Ji P, Shenoy P (2007) Design and analysis of a demand adaptive and locality aware streaming media server cluster. Multimedia Systems 13:235–249CrossRefGoogle Scholar
  17. 17.
    Gill P, Shi L, Mahanti A, Li Z, Eager D (2008) Scalable on-demand media streaming for heterogeneous clients. ACM Trans Multimed Comput Commun Appl (ACM TOMCCAP) 5(1):1–24CrossRefGoogle Scholar
  18. 18.
    Hua KA, Cai Y, Sheu S (1998) Patching: a multicast technique for true Video-on-Demand services. In: Proc. of ACM multimedia, pp 191–200Google Scholar
  19. 19.
    Hua KA, Sheu S (1997) Skyscraper broadcasting: a new broadcasting scheme for metropolitan Video-on-Demand system. In: Proc. of ACM special interest group on data communication (SIGCOMM), pp 89–100Google Scholar
  20. 20.
    Huang C, Janakiraman R, Xu L (2004) Loss-resilient on-demand media streaming using priority encoding. In: Proc. of ACM multimedia, pp 152–159Google Scholar
  21. 21.
    Jagannathan S, Almeroth KC (2001) The dynamics of price, revenue, and system utilization. In: Proc. of the IFIP/IEEE international conference on management of multimedia networks and services, pp 329–344Google Scholar
  22. 22.
    Juhn L, Tseng L (1997) Harmonic broadcasting for Video-on-Demand service. IEEE Trans Broadcast 43(3):268–271CrossRefGoogle Scholar
  23. 23.
    Ma H, Shin GK, Wu W (2005) Best-effort patching for multicast trueVoD service. Multimed Tools Appl 26(1):101–122CrossRefGoogle Scholar
  24. 24.
    Ma H, Shin KG (2002) Multicast video-on-demand services. ACM SIGCOMM Comput Commun Rev 32(1):31–43CrossRefGoogle Scholar
  25. 25.
    Mei T, Hua X-S, Li S (2009) VideoSense: a contextual in-video advertising system. IEEE Trans Circ Syst Video Technol 19(12):1866–1879CrossRefGoogle Scholar
  26. 26.
    O’Neill JP, Dukes J, Dukes J (2009) Re-evaluating multicast streaming using large-scale network simulation. In: Intensive, pp 39–46Google Scholar
  27. 27.
    Ostrowski JR, Sarhan NJ (2009) Characterization of social video. In: Proc. of SPIE/ACM multimedia computing and networking conference (MMCN)Google Scholar
  28. 28.
    Pallis G, Vakali A (2006) Insight and perspectives for content delivery networks. Commun ACM 49:101–106CrossRefGoogle Scholar
  29. 29.
    Pâris JF, Carter SW, Long DDE (1998) Efficient broadcasting protocols for video on demand. In: Proc. of the int’l symp. on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS), pp 127–132Google Scholar
  30. 30.
    Rayburn D (2007) CDN pricing data: what the CDNs are actually charging for delivery. Online Article. Accessed 16 July 2013
  31. 31.
    Rayburn D (2007) Streaming and digital media: understanding the business and technology. Taylor & Francis USGoogle Scholar
  32. 32.
    Rocha M, Maia M, Cunha I, Almeida J, Campos S (2005) Scalable media streaming to interactive users. In: Proc. of ACM multimedia, pp 966–975Google Scholar
  33. 33.
    Rodrigues CKdS, Leão RMM (2007) Bandwidth usage distribution of multimedia servers using patching. Comput Netw 51(3):569–587CrossRefzbMATHGoogle Scholar
  34. 34.
    Sarhan NJ, Al-Hadrusi MS (2010) Waiting-time prediction and QoSbased pricing for video streaming with advertisements. In: Proc. of IEEE international symposium on multimedia (ISM)Google Scholar
  35. 35.
    Sarhan NJ, Alsmirat MA, Al-Hadrusi M (2010) Waiting-time prediction in scalable on-demand video streaming. ACM Trans Multimed Comput Commun Appl (ACM TOMCCAP) 6(2):1–24CrossRefGoogle Scholar
  36. 36.
    Sarhan NJ, Das CR (2004) A new class of scheduling policies for providing time of service guarantees in Video-On-Demand servers. In: Proc. of the 7th IFIP/IEEE int’l conf. on management of multimedia networks and services, pp 127–139Google Scholar
  37. 37.
    Sarhan NJ, Qudah B (2007) Efficient cost-based scheduling for scalable media streaming. In: Proc. of multimedia computing and networking conf. (MMCN), pp 327–334Google Scholar
  38. 38.
    Schulzrinne H, Rao A, Lanphier R, Lanphier R (1998) Real time streaming protocol (rtsp). In: Internet engineering task force (IETF), RFC2326Google Scholar
  39. 39.
    Thouin F, Coates M (2007) Video-on-demand networks: design approaches and future challenges. IEEE Network 21(2):42–48CrossRefGoogle Scholar
  40. 40.
    Tsiolis AK, Vernon MK (1997) Group-guaranteed channel capacity in multimedia storage servers. In: Proc. of ACM special interest group for the computer systems performance evaluation community (SIGMETRICS), pp 285–297Google Scholar
  41. 41.
    Wu C, Li B, Zhao S, Zhao S (2009) Diagnosing network-wide p2p live streaming inefficiencies. In: INFOCOM, pp 2731–2735Google Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2013

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Multimedia Computing and Networking Research LabWayne State UniversityDetroitUSA

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