Skip to main content
SpringerLink
Log in

3D Video QoE Based Adaptation Framework for Future Communication Networks

  • Conference paper
  • First Online:
IoT Based Control Networks and Intelligent Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 528))

  • 505 Accesses

Abstract

A thorough investigation of the characteristics of 3D videos and the circumstances that support them in content-related contexts is necessary to help advance 3D video adaption systems. In order to create an advanced 3D video Quality of Experience (QoE) based adaptation framework for smart service management of future communication networks, various elements and situations related to content can be employed as milestones. Given this knowledge, the spatial resolution of a color + depth map 3D video representation is taken into account in this study as a factor to suggest a 3D video QoE based adaption framework. In order to construct this framework, the content-related contexts—namely, the motion and structure of a color video and the relative depth and aerial perspective of a depth map are taken into consideration. Under the condition that certain requirements are met and the 3D video QoE is maintained at an ideal level, the performance assessment results obtained using the suggested framework demonstrate its efficacy for selecting the best spatial resolutions for the color + depth map videos.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hewage C, Ekmekcioglu E (2020) Multimedia quality of experience (QoE): current status and future direction. Future Internet 12(7):121. https://doi.org/10.3390/fi12070121

  2. Nur G, Kodikara Arachchi H, Dogan S, Kondoz AM (2012) Advanced adaptation techniques for improvedvideo perception. IEEE Trans Circ Syst Video Technol 22:225–240

    Google Scholar 

  3. Ramakrishna M, Fernandes RC, Karunakar AK (2017) Estimation of adaptation parameters for scalable video streaming over software defined networks. Procedia Comput Sci 115:715–722

    Article  Google Scholar 

  4. Ginimav I (2020) Live streaming architectures for video data-a review. J IoT Soc Mob Anal Cloud 2(4):207–215

    Google Scholar 

  5. Raj JS, Vijesh Joe C (2021) Wi-Fi network profiling and QoS assessment for real time video streaming. IRO J Sustain Wirel Syst 3(1):21–30

    Google Scholar 

  6. Mysirlidis C, Dagiuklas T, Politis I, Ekmekcioglu E, Dogan S, Kotsopoulos S (2014) Quality evaluation of 3D video using colour-plus-depth & MDC over IP networks. IEEE 3DTV

    Google Scholar 

  7. Lie W-N, Lu Y-H (2015) Fast encoding of 3D color-plus-depth video based on 3D-HEVC. In: International conference on image processing

    Google Scholar 

  8. Malekmohamadi H, Fernando A, Kondoz A (2014) A new reduced reference metric for color plus depth 3D video. J Vis Commun Image Represent 25(3):534–541

    Article  Google Scholar 

  9. Peng WH, Zao JK, Huang HT, Wang TW, Huang LS (2008) A rate-distortion optimization model for SVCinter-layer encoding and Bitstream extraction. J Visual Commun Image Represent 19:543–557

    Article  Google Scholar 

  10. Quality of service enhancement for multimedia applications using scalable video coding. In: Second international conference on intelligent computing and control systems (ICICCS)

    Google Scholar 

  11. Fleet DJ, Wiess Y (2006) Optical flow estimation in Paragios. In: Handbook of math. models in comp vision. Springer

    Google Scholar 

  12. Nur G, Dogan S, Kodikara Arachchi H, Kondoz AM (2011) Extended VQM model for predicting 3D video quality considering ambient illumination context. In: IEEE 3DTV conference: the true vision - capture, transmission and display of 3D video, Antalya, Turkey, 16–18 May 2011.

    Google Scholar 

  13. Shi J, Tomasi C (2004) Good features to track. In: IEEE conference on computer vision and pattern recognition, Seattle, pp 593–600

    Google Scholar 

  14. Grigorescu C, Petkov N, Westenberg MA (2004) Contour and boundary detection improved by surround suppression of texture edges. Image Vis Comput 22:609–622

    Article  Google Scholar 

  15. Nur Yilmaz G, Battisti F (2018) Depth perception prediction of 3D video for ensuring advanced multimedia services. In: IEEE 3DTV conference: the true vision - capture, transmission and display of 3D video, Stockholm-Helsinki, Sweden-Finland, 3–5 June 2018

    Google Scholar 

  16. Hassani H, Howell G (2010) A note on standard deviation and standard error. Teach Math Appl 29(2):108–112

    Google Scholar 

  17. Nur Yilmaz G (2018) Depth perception prediction of 3D video QoE for future internet services. In: IEEE 32nd international conference on information networking, Chiang Mai, Thailand, 10–12 January 2018

    Google Scholar 

  18. JSVM (n.d.) 9.13.1 Software, downloaded from CVS Server,garcon.ient.rwth-aachen.de/cvs/jv

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, TED University, Ankara, Turkey

    Gokce Nur Yilmaz & Yucel Cimtay

Authors
  1. Gokce Nur Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yucel Cimtay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gokce Nur Yilmaz .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, St. Joseph's College of Engineering and Technology, Palai, Kerala, India

    P. P. Joby

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. Gerald Schwartz School of Business, Saint Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yilmaz, G.N., Cimtay, Y. (2023). 3D Video QoE Based Adaptation Framework for Future Communication Networks. In: Joby, P.P., Balas, V.E., Palanisamy, R. (eds) IoT Based Control Networks and Intelligent Systems. Lecture Notes in Networks and Systems, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-19-5845-8_54

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5845-8_54

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5844-1

  • Online ISBN: 978-981-19-5845-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation