Abstract
Because of bandwidth and buffer limitation in wireless network, rebuffering events and bitrate drop often cause video impairments, e.g. compression artifacts and video stalling. Hence, these problems often make a loss of the quality of experience (QoE). For making a prediction about the impact of video impairments on QoE, a continuous predictive model for QoE in wireless video streaming is proposed. In this paper, the inputs are composed of three vectors that are the quality of video frame, rebuffering events state and human memory effect, and the output represents the predicted continuous QoE. We build the predictive model by a Hammerstein-Wiener model. Experimental results show that the proposed model can accurately make a prediction about continuous subjective QoE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghadiyaram, D., Pan, J., Bovik, A.C.: A subjective and objective study of stalling events in mobile streaming videos. IEEE Trans. Circuits Syst. Video Technol. 29(1), 183–197 (2019)
Oyman, O., Singh, S.: Quality of experience for HTTP adaptive streaming services. IEEE Commun. Mag. 50(4), 20–27 (2012)
Seufert, M., Egger, S., Slanina, M., et al.: A survey on quality of experience of HTTP adaptive streaming. IEEE Commun. Surv. Tutor. 17(1), 469–492 (2015)
ISO/IEC: ISO/IEC 23009-1 Dynamics Adaptive Streaming Over HTTP (DASH) (2014)
Tavakoli, S., Brunnstrom, K., Gutierrez, J., et al.: Quality of experience of adaptive video streaming: investigation in service parameters and subjective quality assessment methodology. Sig. Process. Image Commun. 39, 432–443 (2015)
Garcia, M.N., Simone, F.D., Tavakoli, S., et al.: Quality of experience and HTTP adaptive streaming: a review of subjective studies. In: 2014 Sixth International Workshop on Quality of Multimedia Experience, Germany, pp. 141–146 (2014)
Tavakoli, S., Egger, S., Seufert, M., et al.: Perceptual quality of HTTP adaptive streaming strategies: cross-experimental analysis of multi-laboratory and crowdsourced subjective studies. IEEE J. Sel. Areas Commun. 34(8), 2141–2153 (2016)
Moorthy, A.K., Choi, L.K., Bovik, A.C., et al.: Video quality assessment on mobile devices: subjective, behavioral and objective studies. IEEE J. Sel. Top. Sig. Process. 6(6), 652–671 (2012)
Bampis, C.G., Li, Z., Bovik, A.C.: Continuous prediction of streaming video QoE using dynamic networks. IEEE Signal Process. Lett. 24(7), 1083–1087 (2017)
Wu, J., Lin, W., Shi, G.: Orientation selectivity based visual pattern for reduced-reference image quality assessment. Inf. Sci. 351, 18–29 (2016)
Shi, W., Sun, Y., et al.: Spatial and temporal feature-based reduced reference quality assessment for rate-varing videos in wireless networks. Int. J. Pattern Recogn. Artif. Intell. (to appear)
Ghadiyaram, D., Pan, J., Bovik, A.C.: A time-varying subjective quality model for mobile streaming videos with stalling events. In: Applications of Digital Image Processing XXXVIII, pp. 1–8 (2015)
LIVE-Netflix Mobile Video Quality Assessment Database. http://live.ece.utexas.edu/research/LIVE_NFLXStudy/nflx_index.html
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI 1994 Workshop on Knowledge Discovery in Databases, New York, USA, pp. 359–370 (1994)
Hands, D.S., Avons, S.E.: Recency and duration neglect in subjective assessment of television picture quality. Appl. Cogn. Psychol. 15(6), 639–657 (2001)
Greene, J., Prepscius, C., Levy, W.B.: Primacy versus recency in quantitative model activity is the critical distinction. Learn. Mem. 7(1), 48–57 (2000)
Seshadrinathan, K., Bovik, A.C.: Temporal hysteresis model of time varying subjective video quality. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1153–1156 (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Proceedings of Asilomar Conference on Signals, Systems, and Computers, pp. 1398–1402 (2003)
Soundararajan, R., Bovik, A.C.: Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Trans. Circuits Syst. Video Technol. 23(4), 684–694 (2013)
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)
Shi, W., Sun, Y., Pan, J.: Continuous prediction for quality of experience in wireless video streaming. IEEE Access (2019)
Shi, W., Sun, Y., Li, S., Cao, Q., Wang, B.: Spatial and temporal feature-based reduced reference quality assessment for rate-varying videos in wireless networks. Int. J. Pattern Recogn. Artif. Intell. (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Shi, W., Pan, J. (2019). Continuous Predictive Model for Quality of Experience in Wireless Video Streaming. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-36405-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36404-5
Online ISBN: 978-3-030-36405-2
eBook Packages: Computer ScienceComputer Science (R0)