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An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions

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Performance Engineering and Stochastic Modeling (EPEW 2021, ASMTA 2021)

Abstract

We present an analytical framework to obtain the distribution of frozen, low quality, and high quality video in a setting with two video quality levels where the channel is dynamic and the data rate the user can achieve varies with time. The presented model, which is based on multi-regime Markov fluid queues, is also capable of producing the distribution of the excess data present in the playout buffer at the end of the video session duration, which will be wasted. The playout control is assumed to be hysteretic, and the effects of the values of thresholds selected for starting playout, switching to low/high quality levels, and pausing/resuming download on the distribution of video quality and excess data is investigated. The presented model can be extended to quality levels more than two.

This study is supported by İstanbul Technical University Scientific Research Projects Coordination Unit (BAP), grant number MGA-2020-42575.

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References

  1. Cisco annual internet report (2018–2023) white paper. Technical report, Cisco Systems, March 2020. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf. Accessed 15 July 2021

  2. Akar, N.: Performance analysis of an asynchronous transfer mode multiplexer with Markov modulated inputs. Ph.D. thesis, Bilkent University (1993)

    Google Scholar 

  3. Akar, N., Gursoy, O., Horvath, G., Telek, M.: Transient and first passage time distributions of first-and second-order multi-regime Markov fluid queues via ME-fication. Methodol. Comput. Appl. Probab. 23, 1–27 (2020)

    MathSciNet  Google Scholar 

  4. Akar, N., Sohraby, K.: Infinite-and finite-buffer Markov fluid queues: a unified analysis. J. Appl. Probab. 41(2), 557–569 (2004)

    Article  MathSciNet  Google Scholar 

  5. Google: Live encoder settings, bitrates, and resolutions - YouTube Help. https://support.google.com/youtube/answer/2853702. Accessed 15 July 2021

  6. Google: Recommended upload encoding settings - YouTube Help. https://support.google.com/youtube/answer/1722171. Accessed 15 July 2021

  7. He, J., Xue, Z., Wu, D., Wu, D.O., Wen, Y.: CBM: online strategies on cost-aware buffer management for mobile video streaming. IEEE Trans. Multimed. 16(1), 242–252 (2013)

    Article  Google Scholar 

  8. Kalman, M., Steinbach, E., Girod, B.: Adaptive media playout for low-delay video streaming over error-prone channels. IEEE Trans. Circuits Syst. Video Technol. 14(6), 841–851 (2004). https://doi.org/10.1109/TCSVT.2004.828335

    Article  Google Scholar 

  9. Kankaya, H.E., Akar, N.: Solving multi-regime feedback fluid queues. Stoch. Models 24(3), 425–450 (2008)

    Article  MathSciNet  Google Scholar 

  10. Kulkarni, V.G.: Fluid models for single buffer systems. Front. Queueing: Models Appl. Sci. Eng. 321, 338 (1997)

    MATH  Google Scholar 

  11. Liu, A., Lau, V.K.: Exploiting base station caching in MIMO cellular networks: opportunistic cooperation for video streaming. IEEE Trans. Signal Process. 63(1), 57–69 (2014). https://doi.org/10.1109/TSP.2014.2367473

    Article  MathSciNet  MATH  Google Scholar 

  12. Luan, T.H., Cai, L.X., Shen, X.: Impact of network dynamics on user’s video quality: analytical framework and QoS provision. IEEE Trans. Multimed. 12(1), 64–78 (2010)

    Article  Google Scholar 

  13. ParandehGheibi, A., Médard, M., Shakkottai, S., Ozdaglar, A.: Avoiding interruptions-QoE trade-offs in block-coded streaming media applications. In: 2010 IEEE International Symposium on Information Theory, pp. 1778–1782. IEEE (2010)

    Google Scholar 

  14. Ramaswami, V., Woolford, D.G., Stanford, D.A.: The Erlangization method for Markovian fluid flows. Ann. Oper. Res. 160(1), 215–225 (2008)

    Article  MathSciNet  Google Scholar 

  15. Schwartz, C., Scheib, M., Hoßfeld, T., Tran-Gia, P., Gimenez-Guzman, J.M.: Trade-offs for video-providers in LTE networks: smartphone energy consumption vs wasted traffic. In: 2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN), pp. 1–6. IEEE (2013)

    Google Scholar 

  16. Sieber, C., Hoßfeld, T., Zinner, T., Tran-Gia, P., Timmerer, C.: Implementation and user-centric comparison of a novel adaptation logic for dash with SVC. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 1318–1323. IEEE (2013)

    Google Scholar 

  17. da Silva Soares, A., Latouche, G.: Fluid queues with level dependent evolution. Eur. J. Oper. Res. 196(3), 1041–1048 (2009)

    Article  MathSciNet  Google Scholar 

  18. Xu, Y., Altman, E., El-Azouzi, R., Elayoubi, S.E., Haddad, M.: QoE analysis of media streaming in wireless data networks. In: Bestak, R., Kencl, L., Li, L.E., Widmer, J., Yin, H. (eds.) NETWORKING 2012. LNCS, vol. 7290, pp. 343–354. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30054-7_27

    Chapter  Google Scholar 

  19. Yazici, M.A.: Markov fluid queue model for video freeze probability in a random environment. In: 14th International Conference on Queueing Theory and Network Applications (QTNA) (2019)

    Google Scholar 

  20. Yazici, M.A., Akar, N.: The workload-dependent MAP/PH/1 queue with infinite/finite workload capacity. Perform. Eval. 70(12), 1047–1058 (2013)

    Article  Google Scholar 

  21. Yazici, M.A., Akar, N.: The finite/infinite horizon ruin problem with multi-threshold premiums: a Markov fluid queue approach. Ann. Oper. Res. 252(1), 85–99 (2017)

    Article  MathSciNet  Google Scholar 

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Correspondence to Mehmet Akif Yazici .

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Yazici, M.A. (2021). An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions. In: Ballarini, P., Castel, H., Dimitriou, I., Iacono, M., Phung-Duc, T., Walraevens, J. (eds) Performance Engineering and Stochastic Modeling. EPEW ASMTA 2021 2021. Lecture Notes in Computer Science(), vol 13104. Springer, Cham. https://doi.org/10.1007/978-3-030-91825-5_29

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  • DOI: https://doi.org/10.1007/978-3-030-91825-5_29

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