American Institute of Mathematical Sciences

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October  2012, 8(4): 807-819. doi: 10.3934/jimo.2012.8.807

Effect of application-layer rate-control mechanism on video quality for streaming services

Marino Mitsumura 1, , Hiroyuki Masuyama 2, , Shoji Kasahara 2, and  Yutaka Takahashi 2,

1. 

Graduate school of Informatics, Kyoto University, Yoshida-Hommachi, Sakyo-ku, Kyoto 606-8501, Japan
2. 

Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501

Received  September 2011 Revised  July 2012 Published  September 2012

In video streaming, feedback-based rate control is utilized for guaranteeing video quality on an end-to-end basis. This paper considers the effect of feedback-based rate controls on video quality from queueing theoretical point of view. We focus on a video streaming mechanism with a feedback-based rate control where a video server regulates the packet-sending rate according to the number of data blocks stored in a client node. The client node has two buffers: a synchronization buffer and a receiver buffer. Packets arriving to the client node are stored in the synchronization buffer first, and a video-data block is retrieved from a fixed number of packets in the synchronization buffer and forwarded to the receiver buffer. We model the client node as a discrete-time two-queue concatenated system with state-dependent packet arrivals, deriving the starvation and overflow probabilities. Numerical examples show the effectiveness of the feedback-based rate controls for improving both these probabilities. In particular, the rate control which is not sensitive to the number of data blocks in the receiver buffer makes these probabilities significantly small.
Keywords: Markov chain, Queueing theory, performance evaluation, rate control., streaming system.
Mathematics Subject Classification: Primary: 60K25, 60J10; Secondary: 68M2.
Citation: Marino Mitsumura, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. Effect of application-layer rate-control mechanism on video quality for streaming services. Journal of Industrial & Management Optimization, 2012, 8 (4) : 807-819. doi: 10.3934/jimo.2012.8.807
References:
[1]

P. de Cuetos and K. W. Ross, Unified framework for optimal video streaming, IEEE INFOCOM'04, 3 (2004), 1479-1489. Google Scholar

[2]

K. A. Hua, M. A. Tantaoui and W. Tavanapong, Video delivery technologies for large-scale deployment of multimedia applications, Proc. IEEE, 92 (2004), 1439-1451. doi: 10.1109/JPROC.2004.832954.  Google Scholar

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[4]

D. Jurca, J. Chakareski, J. P. Wagner and P. Frossard, Enabling adaptive video streaming in P2P systems, IEEE Commun. Mag., 45 (2007), 108-114. doi: 10.1109/MCOM.2007.374427.  Google Scholar

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H. Kanakia, P. P. Mishra and A. R. Reibman, An adaptive congestion control scheme for real time packet video transport, IEEE/ACM Trans. Networking, 3 (1995), 671-682. Google Scholar

[6]

L. S. Lam, J. Y. B. Lee, S. C. Liew and W. Wang, "A Transparent Rate Adaption Algorithm for Streaming Video over the Internet," Proc. of the 18th International conference on advanced information networking and applications (AINA' 04), 20044. Google Scholar

[7]

G. Latouche, P. A. Jacobs and D. P. Gaver, Finite Markov chain models skip-free in one direction, Naval Research Logistics Quarterly, 31 (1984), 571-588. doi: 10.1002/nav.3800310407.  Google Scholar

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D. Marpe, T. Wiegand and G. J. Sullivan, The H.264/MPEG4 advanced video voding standard and its applications, IEEE Commun. Mag., 44 (2006), 134-143. doi: 10.1109/MCOM.2006.1678121.  Google Scholar

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I. Moccagatta, S. Soudagar, J. Liang and H. Chen, Error-resilient coding in JPEG-2000 and MPEG-4, IEEE J. Sel. Areas Commun., 18 (2000), 899-914. doi: 10.1109/49.848245.  Google Scholar

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J. D. Salehi, Z. L. Zhang, J. Kurose and D. Towsley, Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing, IEEE/ACM Trans. Netw., 6 (1998), 397-410. Google Scholar

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show all references

References:
[1]

P. de Cuetos and K. W. Ross, Unified framework for optimal video streaming, IEEE INFOCOM'04, 3 (2004), 1479-1489. Google Scholar

[2]

K. A. Hua, M. A. Tantaoui and W. Tavanapong, Video delivery technologies for large-scale deployment of multimedia applications, Proc. IEEE, 92 (2004), 1439-1451. doi: 10.1109/JPROC.2004.832954.  Google Scholar

[3]

C. Huang and L. Xu, SRC: Stable rate control for streaming media, IEEE GLOBECOM'03, 7 (2003), 4016-4021. Google Scholar

[4]

D. Jurca, J. Chakareski, J. P. Wagner and P. Frossard, Enabling adaptive video streaming in P2P systems, IEEE Commun. Mag., 45 (2007), 108-114. doi: 10.1109/MCOM.2007.374427.  Google Scholar

[5]

H. Kanakia, P. P. Mishra and A. R. Reibman, An adaptive congestion control scheme for real time packet video transport, IEEE/ACM Trans. Networking, 3 (1995), 671-682. Google Scholar

[6]

L. S. Lam, J. Y. B. Lee, S. C. Liew and W. Wang, "A Transparent Rate Adaption Algorithm for Streaming Video over the Internet," Proc. of the 18th International conference on advanced information networking and applications (AINA' 04), 20044. Google Scholar

[7]

G. Latouche, P. A. Jacobs and D. P. Gaver, Finite Markov chain models skip-free in one direction, Naval Research Logistics Quarterly, 31 (1984), 571-588. doi: 10.1002/nav.3800310407.  Google Scholar

[8]

D. Marpe, T. Wiegand and G. J. Sullivan, The H.264/MPEG4 advanced video voding standard and its applications, IEEE Commun. Mag., 44 (2006), 134-143. doi: 10.1109/MCOM.2006.1678121.  Google Scholar

[9]

I. Moccagatta, S. Soudagar, J. Liang and H. Chen, Error-resilient coding in JPEG-2000 and MPEG-4, IEEE J. Sel. Areas Commun., 18 (2000), 899-914. doi: 10.1109/49.848245.  Google Scholar

[10]

J. D. Salehi, Z. L. Zhang, J. Kurose and D. Towsley, Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing, IEEE/ACM Trans. Netw., 6 (1998), 397-410. Google Scholar

[11]

H. Yoshida, K. Nogami and K. Satoda, Proposal and evaluation of joint rate control for stored video streaming, Proc. IEEE 2010 International communications quality and reliability (CQR) workshop, (2010), 1-6. Google Scholar

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