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Telfor Journal
2017, vol. 9, br. 1, str. 8-13
jezik rada: engleski
vrsta rada: neklasifikovan
doi:10.5937/telfor1701008M


4K video traffic prediction using seasonal autoregressive modeling
(naslov ne postoji na srpskom)
aSchool of Electrical Engineering, University of Belgrade, Belgrade + Telenor, Belgrade
bUniverzitet u Beogradu, Elektrotehnički fakultet, Katedra za telekomunikacije

e-adresa: 1.dejan.markovic@gmail.com, anaga777@gmail.com, an

Projekat

Razvoj visokokvalitetnih uređaja posebne namene na bazi novih tehnologija kristalnih jedinki (MPNTR - 32048)

Sažetak

(ne postoji na srpskom)
From the perspective of average viewer, high definition video streams such as HD (High Definition) and UHD (Ultra HD) are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding). Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

Ključne reči

4K video traffic; HEVC; autoregressive model; seasonal technique; video tracing; prediction

Reference

*** (2016) Index, Cisco visual networking: Forecast and methodology, 2015-2020 white paper. 1st June
*** Pantagonia 8K. https://www.youtube.com/watch?v=ChOhcHD8fBA, 01.02.2017
*** The project R. http://www.r-project.org, 01.09.2016
Al, T.A.K., Jain, R., So-In, C. (2010) Modeling and generation of AVC and SVC-TS mobile video traces for broadband access networks. u: Proceedings of the first annual ACM SIGMM conference on Multimedia systems - MMSys '10, New York, NY: Association for Computing Machinery (ACM), str. 89
Alexa http://www.alexa.com/topsites, 01.02.2017
Al-Tamimi, A., Jain, R., So-In, C. (2010) Dynamic resource allocation based on online traffic prediction for video streams. u: 2010 IEEE 4th International Conference on Internet Multimedia Services Architecture and Application, Institute of Electrical and Electronics Engineers (IEEE), str. 1-6
Caleb, D. (2014) Your 1080p TV is old already: Everything you need to know about ultra HD 4K. https://www.digitaltrends.com, 01.09.2016
De, la C.L.J., Pallares, E., Alins, J.J., Mata, J. (1998) Self-similar traffic generation using a fractional ARIMA model. Application to the VBR MPEG video traffic. u: ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202), Institute of Electrical and Electronics Engineers (IEEE), str. 102-107
Huang, X., Zhou, Y., Zhang, R. (2004) A Multiscale Model for MPEG-4 Varied Bit Rate Video Traffic. IEEE Transactions on Broadcasting, 50(3): 323-334
Hyndman, R.J., Athanasopoulos, G. (2014) Forecasting: Principles and practice. OTexts
Macedo, M. (2016) Three notes on the SAM video statistical model. 01.09.2016
Markovic, D.R., Gavrovska, A.M., Reljin, I.S. (2016) 4K video traffic analysis using seasonal autoregressive model for traffic prediction. u: 2016 24th Telecommunications Forum (TELFOR), Institute of Electrical and Electronics Engineers (IEEE), str. 1-4
Montgomery, D.C., Johnson, L.A., Gardiner, J.S. (1990) Forecasting and time series analysis. McGraw-Hill Companies, Second Edition, July; 381
Reisslein, M., Karam, L., Seeling, P.H. (2009) 264/AVC and SVC video trace library: A quick reference guide. May
Reisslein, M. (2012) Video trace library. Arizona State University, http://trace.eas.asu.edu, 01.09.2016
Reljin, I., Gavrovska, A. (2016) New trends in imaging and video traffic modeling. u: 2016 13th Symposium on Neural Networks and Applications (NEUREL), Institute of Electrical and Electronics Engineers (IEEE), str. 1-1
Reljin, I., Samčović, A., Reljin, B. (2006) H.264/AVC Video Compressed Traces: Multifractal and Fractal Analysis. EURASIP Journal on Advances in Signal Processing, 2006: 1-14
Seeling, P., Reisslein, M. (2014) Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC. Scientific World Journal, 2014: 1-16
Tamimi, A.K.A., Jain, R., So-In, C. (2010) Statistical analysis and modeling of high definition video traces. u: 2010 IEEE International Conference on Multimedia and Expo, Institute of Electrical and Electronics Engineers (IEEE), str. 596-601
Uhrina, M., Frnda, J., Sevcik, L., Vaculik, M. (2014) Impact of H.264/AVC and H.265/HEVC Compression Standards on the Video Quality for 4K Resolution. Advances in Electrical and Electronic Engineering, 12(4):
Wu, J., Yuen, C., Wang, M., Chen, J. (2016) Content-Aware Concurrent Multipath Transfer for High-Definition Video Streaming over Heterogeneous Wireless Networks. IEEE Transactions on Parallel and Distributed Systems, 27(3): 710-723