- -

New objective QoE models for evaluating ABR algorithms in DASH

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

New objective QoE models for evaluating ABR algorithms in DASH

Show full item record

De Fez Lava, I.; Belda Ortega, R.; Guerri Cebollada, JC. (2020). New objective QoE models for evaluating ABR algorithms in DASH. Computer Communications. 158:126-140. https://doi.org/10.1016/j.comcom.2020.05.011

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166269

Files in this item

Item Metadata

Title: New objective QoE models for evaluating ABR algorithms in DASH
Author: De Fez Lava, Ismael Belda Ortega, Román Guerri Cebollada, Juan Carlos
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia
Issued date:
Embargo end date: 2022-05-11
Abstract:
[EN] As users become more demanding with regards to the consumption of multimedia content, the importance of measuring their level of satisfaction is growing. The difficulty in terms of time and resources for assessing the ...[+]
Subjects: Quality of Experience (QoE) , Dynamic Adaptive Streaming over HTTP (DASH) , Peak Signal-to-Noise Ratio (PSNR) , Video Multimethod Assessment Fusion (VMAF) , Adaptive Bitrate Streaming (ABR) , ITU-T P.1203
Copyrigths: Embargado
Source:
Computer Communications. (issn: 0140-3664 )
DOI: 10.1016/j.comcom.2020.05.011
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.comcom.2020.05.011
Project ID:
UPV/PAID-10-18
Thanks:
This work is supported by the PAID-10-18 Program and by the R&D Line "Technologies for distribution and processing of multimedia information and QoE'' from the Universitat Politecnica de Valencia.
Type: Artículo

References

Cisco webpage, Cisco visual networking index: Forecast and trends, 2017–2022 White Paper, Available online: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html. (Accessed September 2019).

X. Yin, V. Sekar, B. Sinopoli, Toward a principled framework to design dynamic adaptive streaming algorithms over HTTP, in: Proc. of the 13th ACM Workshop on Hot Topics in Networks, HotNets, Los Angeles, CA, USA, Oct., 2014, pp. 1–7.

Barman, N., & Martini, M. G. (2019). QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges. IEEE Access, 7, 30831-30859. doi:10.1109/access.2019.2901778 [+]
Cisco webpage, Cisco visual networking index: Forecast and trends, 2017–2022 White Paper, Available online: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html. (Accessed September 2019).

X. Yin, V. Sekar, B. Sinopoli, Toward a principled framework to design dynamic adaptive streaming algorithms over HTTP, in: Proc. of the 13th ACM Workshop on Hot Topics in Networks, HotNets, Los Angeles, CA, USA, Oct., 2014, pp. 1–7.

Barman, N., & Martini, M. G. (2019). QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges. IEEE Access, 7, 30831-30859. doi:10.1109/access.2019.2901778

Liu, Y., Dey, S., Ulupinar, F., Luby, M., & Mao, Y. (2015). Deriving and Validating User Experience Model for DASH Video Streaming. IEEE Transactions on Broadcasting, 61(4), 651-665. doi:10.1109/tbc.2015.2460611

Chikkerur, S., Sundaram, V., Reisslein, M., & Karam, L. J. (2011). Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison. IEEE Transactions on Broadcasting, 57(2), 165-182. doi:10.1109/tbc.2011.2104671

Ciubotaru, B., Muntean, G.-M., & Ghinea, G. (2009). Objective Assessment of Region of Interest-Aware Adaptive Multimedia Streaming Quality. IEEE Transactions on Broadcasting, 55(2), 202-212. doi:10.1109/tbc.2009.2020448

S. Winkler, A. Sharma, D. Mcnally, Perceptual video quality and blockiness metrics for multimedia streaming applications, in: Proc. of the Int. Symposium on Wireless Personal Multimedia Communications, Aalborg, Denmark, Sep., 2001, pp. 547–552.

Bampis, C. G., & Bovik, A. C. (2018). Feature-based prediction of streaming video QoE: Distortions, stalling and memory. Signal Processing: Image Communication, 68, 218-228. doi:10.1016/j.image.2018.05.017

Soundararajan, R., & Bovik, A. C. (2013). Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing. IEEE Transactions on Circuits and Systems for Video Technology, 23(4), 684-694. doi:10.1109/tcsvt.2012.2214933

A. Raake, M.-N. Garcia, W. Robitza, P. List, S. Göring, B. Feiten, A bitstream-based, scalable video-quality model for HTTP adaptive streaming: ITU-T P.1203.1, in: Proc. of Int. Conf. on Quality of Multimedia Experience, QoMEX, Erfurt, Germany, 2017.

W. Robitza, S. Göring, A. Raake, D. Lindegren, G. Heikkilä, J. Gustafsson, P. List, B. Feiten, U. Wüstenhagen, M.-N. Garcia, K. Yamagishi, S. Broom, HTTP Adaptive Streaming QoE Estimation with ITU-T Rec. P.1203 – Open Databases and Software, in: Proc. of the 9th ACM Multimedia Systems Conference, Amsterdam, Netherlands, Jun., 2018, pp. 466–471.

X. Deng, L. Chen, F. Wang, Z. Fei, W. Bai, C. Chi. G. Han, L. Wan, A novel strategy to evaluate QoE for video service delivered over HTTP adaptive streaming, in: Proc. of the IEEE 80th Vehicular Technology Conference, VTC2014-Fall, Vancouver, BC, Canada, Sep., 2014.

Zegarra Rodriguez, D., Lopes Rosa, R., Costa Alfaia, E., Issy Abrahao, J., & Bressan, G. (2016). Video Quality Metric for Streaming Service Using DASH Standard. IEEE Transactions on Broadcasting, 62(3), 628-639. doi:10.1109/tbc.2016.2570012

M.N. Garcia, W. Robitza, A. Raake, On the accuracy of short term quality models for long-term quality prediction, in: Proc. 7th Int. Workshop Qual. Multimedia Exper., QoMEX, Pylos, Greece, 2015.

Duanmu, Z., Zeng, K., Ma, K., Rehman, A., & Wang, Z. (2017). A Quality-of-Experience Index for Streaming Video. IEEE Journal of Selected Topics in Signal Processing, 11(1), 154-166. doi:10.1109/jstsp.2016.2608329

Bampis, C. G., Li, Z., & Bovik, A. C. (2017). Continuous Prediction of Streaming Video QoE Using Dynamic Networks. IEEE Signal Processing Letters, 24(7), 1083-1087. doi:10.1109/lsp.2017.2705423

Winkler, S., & Mohandas, P. (2008). The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics. IEEE Transactions on Broadcasting, 54(3), 660-668. doi:10.1109/tbc.2008.2000733

Bampis, C. G., Li, Z., & Bovik, A. C. (2019). Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, 29(8), 2256-2270. doi:10.1109/tcsvt.2018.2868262

Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on Image Processing, 15(2), 430-444. doi:10.1109/tip.2005.859378

Li, S., Zhang, F., Ma, L., & Ngan, K. N. (2011). Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments. IEEE Transactions on Multimedia, 13(5), 935-949. doi:10.1109/tmm.2011.2152382

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018

C. Müller, S. Lederer, C. Timmerer, An evaluation of dynamic adaptive streaming over HTTP in vehicular environments, in: Proc. of the 4th Workshop on Mobile Video, MoVid, Chapel Hill, NC, USA, Feb., 2012.

P. Juluri, V. Tamarapalli, D. Medhi, SARA: Segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP, in: Proc. of the IEEE Int. Conf. on Communication Workshop, ICCW, London, UK, Jun., 2015, pp. 1765–1770.

Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., & Zimmermann, R. (2019). A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP. IEEE Communications Surveys & Tutorials, 21(1), 562-585. doi:10.1109/comst.2018.2862938

K. Spiteri, R. Urgaonkar, R. Sitaraman, BOLA: Near-optimal bitrate adaption for online videos, in: Proc. of the Int. Conference on Computer Communications, INFOCOM, San Francisco, CA, USA, Apr., 2016.

Y. Shuai, T. Herfet, A buffer dynamic stabilizer for low-latency adaptive video streaming, in: Proc. of the Int. Conference on Consumer Electronics, Berlin, Germany, Sep., 2016.

Mobile video service performance study, HUAWEI White Paper, available online: http://www.ctiforum.com/uploadfile/2015/0701/20150701091255294.pdf, Published, (Accessed September 2019).

C. Bampis, Measuring video quality with VMAF: Why you should care, in: AOMedia Research Symposium, San Francisco, Oct., 2019.

Ghadiyaram, D., Pan, J., & Bovik, A. C. (2019). A Subjective and Objective Study of Stalling Events in Mobile Streaming Videos. IEEE Transactions on Circuits and Systems for Video Technology, 29(1), 183-197. doi:10.1109/tcsvt.2017.2768542

Tavakoli, S., Egger, S., Seufert, M., Schatz, R., Brunnstrom, K., & Garcia, N. (2016). Perceptual Quality of HTTP Adaptive Streaming Strategies: Cross-Experimental Analysis of Multi-Laboratory and Crowdsourced Subjective Studies. IEEE Journal on Selected Areas in Communications, 34(8), 2141-2153. doi:10.1109/jsac.2016.2577361

C. Moldovan, K. Hagn, C. Sieber, W. Kellerer, T. Hoßfeld, Keep calm and don’t switch: about the relationship between switches and quality in HAS, in: Proc. of the Int. Teletraffic Congress, ITC, Genoa, Italy, Sep., 2017.

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record