Skip to main content
Log in

Inter-Object Layer Clustering for scalable video streaming

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this work, we develop an efficient storage technique to support real-time streaming of layer encoded video in a single hard disk. The size of a single hard disk drive will soon be able to hold multi-tera bytes and is going to handle relatively larger number of files. We expect that disk layout in a single disk will be rather critical issue in determining the efficiency of the storage system. We propose a novel storage technique, Inter-Object Layer Clustering for layer encoded video objects. In Inter-Object Layer Clustering, storage is partitioned into two regions: lower layer partition and upper layer partition. Lower and upper layer partition harbor the lower layer and upper layer data blocks across all video objects and cluster them together. We develop an elaborate performance model for this placement scheme. We examine the performance of the proposed technique using analytical formulation as well as a physical experiment. We found that clustering the layers across all objects brings 100% increase in the number of concurrent sessions compared to the case where file is stored in temporal order when the clients’ access bandwidth is narrow. Inter-Object Layer Clustering shows 15% performance improvement compared to the clustering of layers within the objects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Li,k is the size of \({\mathbb A}_{S_i,k}\), where k > low, in number of cylinders.

  2. \(L_{S_{r_s},lower}\) is the size of \({\mathbb A}_{S_{r_s},lower}\) in number of cylinders.

  3. Assuming that at least one layer (the lowest layer) is transmitted to each client in each round, N lower is equal to N.

References

  1. Anastasiadis SV, Sevcik KC, Stumm M (2005) Scalable and fault-tolerant support for variable bit-rate data in the Exedra streaming server. ACM Transactions on Storage 1(4):419–456

    Article  Google Scholar 

  2. Dai L, Cui Y, Xue Y (2007) Maximizing throughput in layered peer-to-peer streaming. In: IEEE international conference on communications

  3. de Cuetos P, Ross KW (2002) Adaptive rate control for streaming stored fine-grained scalable video. In: Proc. of the 12th international workshop on NOSSDAV. ACM, New York, pp 3–12

    Google Scholar 

  4. Jiang Q, Xi H, Yin B (2007) Dynamic file grouping for load balancing in streaming media clustered server systems. In: International conference on information acquisition

  5. Kang SR, Zhang Y, Dai M, Loguinov D (2004) Multi-layer active queue management and congestion control for scalable video streaming. In: Proc. of the 24th international conference on distributed computing systems

  6. Kang S, Won Y, Roh S (2006) Harmonic placement: file system support for scalable streaming of layer encoded object. In: Proc. of NOSSDAV, Rhode Island, USA

  7. Kee-Yin Ng J, Xiong S, Shen H (2000) A multi-server video-on-demand system with arbitrary rate playback support. J Syst Softw 51(3):217–227

    Article  Google Scholar 

  8. Liu J, Li B, Hou YT, Chlamtac I (2004) On optimal layering and bandwidth allocation for multisession video broadcasting. IEEE Trans Wirel Commun 3(2):656–667

    Article  Google Scholar 

  9. McCanne S, Floyd S (2002) Network simulator. www-mash.cs.berkeley.edu/ns

  10. Radha HM, van der Schaar M, Chen Y (2001) The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP. IEEE Trans Multimedia 3(1):53–58

    Article  Google Scholar 

  11. Real Networks Inc. (2002) Helix producer user’s guide

  12. Rejaie R, Ortega A (2003) PALS: peer-to-peer adaptive layered streaming. In: Proc. of NOSSDAV, Monterey, CA, USA

  13. Rejaie R, Handely M, Estrin D (1999) RAP: An end-to-end rate-based congestion control mechanism for realtime streams in the Internet. In: Proc. of IEEE infocom, NY, USA, pp 1337–1345

  14. Saparilla D, Ross KW (2000) Optimal streaming of layered video. In: Proc. of IEEE INFOCOM

  15. Shenoy PJ, Vin HM (2000) Failure recovery algorithms for multimedia servers. Multimedia Syst 8(1):1–19

    Article  Google Scholar 

  16. Shi L, Sessini P, Mahanti A, Li Z, Eager DL (2006) Scalable streaming for heterogeneous clients. In: Proc. of ACM multimedia, Santa Barbara, CA, USA

  17. Shin D, Yeom HY (2006) Refined modeling of a modern disk drive system. In: The international symposium on performance evaluation of computer and telecommunication systems (SPECTS’06), Calgary, Canada

  18. Siao X, Shi Y, Zhang B, Gao Y (2008) OCals: a novel overlay construction approach for layered streaming. In: IEEE international conference on communications

  19. Song M, Shin H (2006) Replication and retrieval strategies for resource-effective admission control in multi-resolution video servers. Multimedia Tools and Applications 28(3):347–372

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sooyong Kang.

Additional information

This work was supported by National Research Foundation of Korea Grant funded by the Korean Government (2009-0073575).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, H., Yeom, H.Y., Kang, S. et al. Inter-Object Layer Clustering for scalable video streaming. Multimed Tools Appl 50, 313–333 (2010). https://doi.org/10.1007/s11042-009-0384-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-009-0384-7

Keywords

Navigation