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

Mehmet Akif Yazici

doi:10.1007/978-3-030-91825-5_29

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

Mehmet Akif Yazici^*

^*Bu çalışma için yazışmadan sorumlu yazar

Bilişim Enstitüsü

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings
Editörler	Paolo Ballarini, Hind Castel, Ioannis Dimitriou, Mauro Iacono, Tuan Phung-Duc, Joris Walraevens
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	472-487
Sayfa sayısı	16
ISBN (Basılı)	9783030918248
DOI'lar	https://doi.org/10.1007/978-3-030-91825-5_29
Yayın durumu	Yayınlandı - 2021
Etkinlik	17th European Performance Engineering Workshop, EPEW 2021, and the 26th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021 - Virtual, Online Süre: 13 Ara 2021 → 14 Ara 2021

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	13104 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	17th European Performance Engineering Workshop, EPEW 2021, and the 26th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021
Şehir	Virtual, Online
Periyot	13/12/21 → 14/12/21

Bibliyografik not

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-030-91825-5_29

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Yazici, M. A. (2021). An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions. In P. Ballarini, H. Castel, I. Dimitriou, M. Iacono, T. Phung-Duc, & J. Walraevens (Eds.), Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings (pp. 472-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13104 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-91825-5_29

Yazici, Mehmet Akif. / An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions. Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings. editör / Paolo Ballarini ; Hind Castel ; Ioannis Dimitriou ; Mauro Iacono ; Tuan Phung-Duc ; Joris Walraevens. Springer Science and Business Media Deutschland GmbH, 2021. pp. 472-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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.",

keywords = "Markov fluid queue, Quality of experience, Video, Wasted video data",

author = "Yazici, {Mehmet Akif}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 17th European Performance Engineering Workshop, EPEW 2021, and the 26th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021 ; Conference date: 13-12-2021 Through 14-12-2021",

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Yazici, MA 2021, An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions. in P Ballarini, H Castel, I Dimitriou, M Iacono, T Phung-Duc & J Walraevens (eds), Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), hac. 13104 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 472-487, 17th European Performance Engineering Workshop, EPEW 2021, and the 26th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2021, Virtual, Online, 13/12/21. https://doi.org/10.1007/978-3-030-91825-5_29

An Analytical Framework for Video Quality and Excess Data Distribution in Multiple-Quality Video Under Dynamic Channel Conditions. / Yazici, Mehmet Akif.
Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings. ed. / Paolo Ballarini; Hind Castel; Ioannis Dimitriou; Mauro Iacono; Tuan Phung-Duc; Joris Walraevens. Springer Science and Business Media Deutschland GmbH, 2021. p. 472-487 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13104 LNCS).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

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AB - 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.

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Yazici MA. 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, editörler, Performance Engineering and Stochastic Modeling - 17th European Workshop, EPEW 2021, and 26th International Conference, ASMTA 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 472-487. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-91825-5_29