Abstract
HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase.
- Anne Aaron, Zhi Li, Megha Manohara, Jan De Cock, and David Ronca. 2015. Per-title encode optimization. Retrieved from February 1, 2018 from https://medium.com/netflix-techblog/per-title-encode-optimization-7e99442b62a2.Google Scholar
- C. G. Bampis, Z. Li, I. Katsavounidis, and A. C. Bovik. 2018. Recurrent and dynamic models for predicting streaming video quality of experience. IEEE Trans. Image Process. 27, 7 (Jul. 2018), 3316–3331. DOI:https://doi.org/10.1109/TIP.2018.2815842Google Scholar
Cross Ref
- C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron, and A. C. Bovik. 2017. Study of temporal effects on subjective video quality of experience. IEEE Trans. Image Process. 26, 11 (Nov. 2017), 5217–5231. DOI:https://doi.org/10.1109/TIP.2017.2729891Google Scholar
Digital Library
- N. Barman and M. G. Martini. 2019. QoE modeling for HTTP adaptive video streaming—A survey and open challenges. IEEE Access 7 (Mar. 2019), 30831–30859.Google Scholar
- A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann. 2019. A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surv. Tutor. 21, 1 (Firstquarter 2019), 562–585.Google Scholar
Cross Ref
- Chao Chen, Lark Kwon Choi, Gustavo De Veciana, Constantine Caramanis, Robert W. Heath, and Alan C. Bovik. 2014. Modeling the time-varying subjective quality of HTTP video streams with rate adaptations. IEEE Trans. Image Process. 23, 5 (May 2014), 2206–2221.Google Scholar
- Giuseppe Cofano, Luca De Cicco, Thomas Zinner, Anh Nguyen-Ngoc, Phuoc Tran-Gia, and Saverio Mascolo. 2017. Design and performance evaluation of network-assisted control strategies for HTTP adaptive streaming. ACM Trans. Multimedia Comput. Commun. Appl. 13, 3s, Article 42 (Jun. 2017), 24 pages. DOI:https://doi.org/10.1145/3092836Google Scholar
Digital Library
- Tom Dietterich. 1995. Overfitting and undercomputing in machine learning. Comput. Surv. 27, 3 (Sept. 1995), 326–327. DOI:https://doi.org/10.1145/212094.212114Google Scholar
Digital Library
- Z. Duanmu, W. Liu, D. Chen, Z. Li, Z. Wang, Y. Wang, and W. Gao. 2019. A knowledge-driven quality-of-experience model for adaptive streaming videos. arXiv:1911.07944 https://arxiv.org/abs/1911.07944.Google Scholar
- Z. Duanmu, W. Liu, D. Chen, Z. Li, Z. Wang, Y. Wang, and W. Gao. 2019. A knowledge-driven quality-of-experience model for adaptive streaming videos. Retrieved June 1, 2020 from https://github.com/zduanmu/ksqi.Google Scholar
- Z. Duanmu, K. Ma, and Z. Wang. 2018. Quality-of-experience for adaptive streaming videos: An expectation confirmation theory motivated approach. IEEE Trans. Image Process. 27, 12 (Dec. 2018), 6135–6146. DOI:https://doi.org/10.1109/TIP.2018.2855403Google Scholar
Cross Ref
- Z. Duanmu, A. Rehman, and Z. Wang. 2018. A quality-of-experience database for adaptive video streaming. IEEE Trans. Broadcast. 64, 2 (Jun. 2018), 474–487. DOI:https://doi.org/10.1109/TBC.2018.2822870Google Scholar
Cross Ref
- Z. Duanmu, K. Zeng, K. Ma, A. Rehman, and Z. Wang. 2017. A quality-of-experience index for streaming video. IEEE J. Select. Top. Sign. Process. 11, 1 (Feb. 2017), 154–166. DOI:https://doi.org/10.1109/JSTSP.2016.2608329Google Scholar
Cross Ref
- N. Eswara, S. Ashique, A. Panchbhai, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, and S. S. Channappayya. 2019. Streaming video QoE modeling and prediction: A long short-term memory approach. Retrieved June 1, 2020 from https://github.com/lfovia/lstm_qoe.Google Scholar
- N. Eswara, S. Ashique, A. Panchbhai, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, and S. S. Channappayya. 2020. Streaming video QoE modeling and prediction: A long short-term memory approach. IEEE Trans. Circ. Syst. Vid. Technol. 30, 3 (Mar. 2020), 661–673.Google Scholar
- N. Eswara, K. Manasa, A. Kommineni, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, and S. S. Channappayya. 2018. A continuous qoe evaluation framework for video streaming over HTTP. IEEE Trans. Circ. Syst. Vid. Technol. 28, 11 (Nov. 2018), 3236–3250. DOI:https://doi.org/10.1109/TCSVT.2017.2742601Google Scholar
Digital Library
- D. Ghadiyaram, J. Pan, and A. C. Bovik. 2018. Learning a continuous-time streaming video QoE model. IEEE Trans. Image Process. 27, 5 (May 2018), 2257–2271.Google Scholar
Cross Ref
- D. Ghadiyaram, J. Pan, and A. C. Bovik. 2019. A subjective and objective study of stalling events in mobile streaming videos. IEEE Trans. Circ. Syst. Vid. Technol. 29, 1 (Jan. 2019), 183–197.Google Scholar
- Zhili Guo, Yao Wang, and Xiaoqing Zhu. 2015. Assessing the visual effect of non-periodic temporal variation of quantization stepsize in compressed video. In Proceedings of the IEEE International Conference on Image Processing (ICIP’15). 3121–3125.Google Scholar
Digital Library
- Tobias Hoßfeld, Raimund Schatz, Ernst Biersack, and Louis Plissonneau. 2013. Internet video delivery in YouTube: From traffic measurements to quality of experience. Data Traffic Monitor. Anal. 7754 (2013), 264–301.Google Scholar
Cross Ref
- Tobias Hoßfeld, Michael Seufert, Christian Sieber, and Thomas Zinner. 2014. Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming. In Proceedings of the 6th International Workshop on Quality of Multimedia Experience (QoMEX’14). 111–116.Google Scholar
Cross Ref
- T. Huang, C. Zhou, X. Yao, R. Zhang, C. Wu, and L. Sun. 2020. Quality-aware neural adaptive video streaming with lifelong imitation learning. IEE Journal on Selected Areas in Communications 38, 10 (2020), 2324--2342. DOI:10.110/JSAC.2020.300363Google Scholar
Cross Ref
- P. Juluri, V. Tamarapalli, and D. Medhi. 2016. Measurement of quality of experience of video-on-demand services: A survey. IEEE Commun. Surv. Tutor. 18, 1 (Feb. 2016), 401–418. Google Scholar
Cross Ref
- Daniel Kahneman, Barbara L. Fredrickson, Charles A. Schreiber, and Donald A. Redelmeier. 1993. When more pain is preferred to less: Adding a better end. Psychol. Sci. 4, 6 (Nov. 1993), 401–405.Google Scholar
Cross Ref
- Friedemann Köster, Gabriel Mittag, and Sebastian Möller. 2017. Modeling the overall quality of experience on the basis of underlying quality dimensions. In Proceedings of the 9th International Conference Quality Multimedia Experience. 1–6.Google Scholar
Cross Ref
- Patrick Le Callet, Sebastian Möller, and Andrew Perkis (eds.). 2013. Qualinet White Paper on Definitions of Quality of Experience. Technical Report. Version 1.2.Google Scholar
- Zhi Li, Christos Bampis, Julie Novak, Anne Aaron, Kyle Swanson, Anush Moorthy, and J. D. Cock. 2018. VMAF: The journey continues. Retrieved June 1, 2020 from https://netflixtechblog.com/vmaf-the-journey-continues-44b51ee9ed12.Google Scholar
- Zhi Li, Christos Bampis, Julie Novak, Anne Aaron, Kyle Swanson, Anush Moorthy, and J. D. Cock. 2019. VMAF—Video multi-method assessment fusion. Retrieved June 1, 2020 from https://github.com/Netflix/vmaf.Google Scholar
- David Lindegren, Werner Robitza, Marie-Neige Garcia, Steve Göring, Alexander Raake, Peter List, Bernhard Feiten, Ulf Wüstenhagen, Jörgen Gustafsson, Gunnar Heikkilä, Junaid Shaikh, and Simon Broom. 2018. ITU-T Rec. P.1203 Standalone Implementation. Retrieved July 1, 2020 from https://github.com/itu-p1203/itu-p1203/.Google Scholar
- Yao Liu, Sujit Dey, Fatih Ulupinar, Michael Luby, and Yinian Mao. 2015. Deriving and validating user experience model for DASH video streaming. IEEE Trans. Broadcast. 61, 4 (Dec. 2015), 651–665.Google Scholar
Cross Ref
- M. Hammad Mazhar and M. Zubair Shafiq. 2018. Real-time video quality of experience monitoring for HTTPS and QUIC. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18). 1331–1339.Google Scholar
- Christopher Müller, Stefan Lederer, and Christian Timmerer. 2012. An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. In Proceedings of the 4th Workshop on Mobile Video. 37–42.Google Scholar
Digital Library
- Hyunwoo Nam, Kyung-Hwa Kim, and Henning Schulzrinne. 2016. QoE matters more than QoS: Why people stop watching cat videos. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications. 1–9.Google Scholar
Digital Library
- Y. Ou, Y. Xue, and Y. Wang. 2014. Q-STAR: A perceptual video quality model considering impact of spatial, temporal, and amplitude resolutions. IEEE Trans. Image Process. 23, 6 (Jun. 2014), 2473–2486.Google Scholar
Digital Library
- Lloyd Peterson and Margaret Jean Peterson. 1959. Short-term retention of individual verbal items.J. Exp. Psychol. 58, 3 (Sep. 1959), 193–198.Google Scholar
Cross Ref
- Stefano Petrangeli, Jeroen Famaey, Maxim Claeys, Steven Latré, and Filip De Turck. 2015. QoE-driven rate adaptation heuristic for fair adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 12, 2, Article 28 (Oct. 2015), 24 pages. DOI:https://doi.org/10.1145/2818361Google Scholar
Digital Library
- Alexander Raake, Marie-Neige Garcia, Werner Robitza, Peter List, Steve Göring, and Bernhard Feiten. 2017. A bitstream-based, scalable video-quality model for HTTP adaptive streaming: ITU-T P.1203.1. In Proceedings of the Ninth International Conference on Quality of Multimedia Experience (QoMEX’17). 1–6.Google Scholar
Cross Ref
- Recommendation ITU-R BT.500-13. 2012. Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union (2012).Google Scholar
- Recommendation ITU-T P.1203.1. 2017. Parametric bitstream-based quality assessment of progressive dowload and adaptive audiovisual streaming services over reliable transport-Video quality estimation module. International Telecommunication Union (2017).Google Scholar
- Recommendation ITU-T P.1203.2. 2017. Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport—Audio quality estimation module. International Telecommunication Union (2017).Google Scholar
- Recommendation ITU-T P.1203.3. 2017. Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport-Quality integration module. International Telecommunication Union (2017).Google Scholar
- Recommendation ITU-T P.1401. 2012. Methods, metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models. International Telecommunication Union (2012).Google Scholar
- Recommendation ITU-T P.880. 2004. Methods for objective and subjective assessment of quality: Continous evaluation of time varying speech quality. International Telecommunication Union (2004).Google Scholar
- Recommendation ITU-T P.913. 2014. Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment. International Telecommunication Union (2014).Google Scholar
- A. Rehman and Z. Wang. 2013. Perceptual experience of time-varying video quality. In Proceedings of the 2013 5th International Workshop on Quality of Multimedia Experience (QoMEX’13). 218–223.Google Scholar
- Russell Revlin. 2012. Cognition: Theory and Practice. Macmillan.Google Scholar
- Werner Robitza, Marie-Neige Garcia, and Alexander Raake. 2017. A modular HTTP adaptive streaming QoE model-Candidate for ITU-T P. 1203 (“P. NATS”). In Proceedings of the 9th International Conference on Quality of Multimedia Experience (QoMEX’17). 1–6.Google Scholar
- Werner Robitza, Steve Göring, Alexander Raake, David Lindegren, Gunnar Heikkilä, Jörgen Gustafsson, Peter List, Bernhard Feiten, Ulf Wüstenhagen, Marie-Neige Garcia, Kazuhisa Yamagishi, and Simon Broom. 2018. HTTP adaptive streaming QoE estimation with ITU-T Rec. P.1203—Open databases and software. In Proceedings of the 9th ACM Multimedia Systems Conference. 466–471. DOI:https://doi.org/10.1145/3204949.3208124Google Scholar
Digital Library
- Demóstenes Zegarra Rodríguez, Renata Lopes Rosa, Eduardo Costa Alfaia, Julia Issy Abrahão, and Graça Bressan. 2016. Video quality metric for streaming service using DASH standard. IEEE Trans. Broadcast. 62, 3 (Sept. 2016), 628–639.Google Scholar
- K. Seshadrinathan and A. C. Bovik. 2011. Temporal hysteresis model of time varying subjective video quality. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’11). 1153–1156. DOI:https://doi.org/10.1109/ICASSP.2011.5946613Google Scholar
- K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack. 2010. Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19, 6 (Jun. 2010), 1427–1441.Google Scholar
Digital Library
- M. Seufert, Pedro Casas, Nikolas Wehner, Gang Li, and Li Kuang. 2019. Stream-based machine learning for real-time QoE analysis of encrypted video streaming traffic. In Proceedings of the 3rd International Workshop on Quality of Experience Management (QoE-Management’19). 76–81.Google Scholar
Cross Ref
- Michael Seufert, Sebastian Egger, Martin Slanina, Thomas Zinner, Tobias Hoßfeld, and Phuoc Tran-Gia. 2015. A survey on quality of experience of HTTP adaptive streaming. IEEE Commun. Surv. Tutor. 17, 1 (2015), 469–492.Google Scholar
Digital Library
- M. Seufert, M. Slanina, S. Egger, and M. Kottkamp. 2013. “To pool or not to pool”: A comparison of temporal pooling methods for HTTP adaptive video streaming. In Proceedings of the 5th International Conference Quality Multimedia Experience. 52–57.Google Scholar
- Kamal Deep Singh, Yassine Hadjadj-Aoul, and Gerardo Rubino. 2012. Quality of experience estimation for adaptive HTTP/TCP video streaming using H. 264/AVC. In Proceedings of the 2012 IEEE Consumer Communications and Networking Conference (CCNC’12). 127–131.Google Scholar
Cross Ref
- M. Takagi, H. Fujii, and A. Shimizu. 2014. Optimized spatial and temporal resolution based on subjective quality estimation without encoding. In Proceedings of the 2014 IEEE Visual Communications and Image Processing Conference. 33–36.Google Scholar
- Samira Tavakoli, Sebastian Egger, Michael Seufert, Raimund Schatz, Kjell Brunnström, and Narciso García. 2016. Perceptual quality of HTTP adaptive streaming strategies: Cross-experimental analysis of multi-laboratory and crowdsourced subjective studies. IEEE J. Select. Areas Commun. 34, 8 (Aug. 2016), 2141–2153.Google Scholar
Digital Library
- Truong Cong Thang, Hung T. Le, Hoc X Nguyen, Anh T. Pham, Jung Won Kang, and Yong Man Ro. 2013. Adaptive video streaming over HTTP with dynamic resource estimation. J. Commun. Netw. 15, 6 (Dec. 2013), 635–644.Google Scholar
Cross Ref
- H. T. T. Tran, Nam Pham Ngoc, Tobias Hoßfeld, and Truong Cong Thang. 2018. A cumulative quality model for HTTP adaptive streaming. In Proceedings of the 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX’18). 1–6.Google Scholar
Cross Ref
- H. T. T. Tran, Nam Pham Ngoc, Yong Ju Jung, Anh T. Pham, and Truong Cong Thang. 2017. A histogram-based quality model for HTTP adaptive streaming. IEICE Trans. Fundam. Electr. Commun. Comput. Sci. E100.A, 2 (Feb. 2017), 555–564.Google Scholar
Cross Ref
- H. T. T. Tran, N. P. Ngoc, A. T. Pham, and T. C. Thang. 2016. A multi-factor QoE model for adaptive streaming over mobile networks. In Proceedings of the 2016 IEEE Globecom Workshops (GC Wkshps’16). 1–6.Google Scholar
- Huyen T. T. Tran, Nam Pham Ngoc, and Truong Cong Thang. 2020. A study on impacts of multiple factors on video qualify of experience. arxiv:2006.12697. Retrieved from https://arxiv.org/abs/2006.12697.Google Scholar
- J. De Vriendt, D. De Vleeschauwer, and D. Robinson. 2013. Model for estimating QoE of video delivered using HTTP adaptive streaming. In Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management (IM’13). 1288–1293.Google Scholar
- Chen Wang, Jianfeng Guan, Tongtong Feng, Neng Zhang, and Tengfei Cao. 2019. BitLat: Bitrate-adaptivity and latency-awareness algorithm for live video streaming. In Proceedings of the 27th ACM International Conference on Multimedia. 2642–2646. DOI:https://doi.org/10.1145/3343031.3356069Google Scholar
Digital Library
- S. Wassermann, M. Seufert, P. Casas, L. Gang, and K. Li. 2019. Let me decrypt your beauty: Real-time prediction of video resolution and bitrate for encrypted video streaming. In Proceedings of the Network Traffic Measurement and Analysis Conference (TMA’19). 199–200.Google Scholar
- M. Xu, C. Li, Z. Chen, Z. Wang, and Z. Guan. 2019. Assessing visual quality of omnidirectional videos. IEEE Trans. Circ. Syst. Vid. Technol. 29, 12 (Dec. 2019), 3516–3530.Google Scholar
- K. Yamagishi and T. Hayashi. 2017. Parametric quality-estimation model for adaptive-bitrate-streaming services. IEEE Trans. Multimedia 19, 7 (Jul. 2017), 1545–1557.Google Scholar
Cross Ref
- Hema Kumar Yarnagula, Parikshit Juluri, Sheyda Kiani Mehr, Venkatesh Tamarapalli, and Deep Medhi. 2019. QoE for mobile clients with segment-aware rate adaptation algorithm (SARA) for DASH video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 15, 2, Article 36 (Jun. 2019), 23 pages. DOI:https://doi.org/10.1145/3311749Google Scholar
Digital Library
- Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. ACM SIGCOMM Comput. Commun. Rev. 45, 4 (Aug. 2015), 325–338.Google Scholar
Digital Library
- L. Yu, T. Tillo, and J. Xiao. 2017. QoE-driven dynamic adaptive video streaming strategy with future information. IEEE Trans. Broadcast. 63, 3 (Sept. 2017), 523–534.Google Scholar
Cross Ref
- T. Zhao, Q. Liu, and C. W. Chen. 2017. QoE in video transmission: A user experience-driven strategy. IEEE Commun. Surv. Tutor. 19, 1 (Firstquarter 2017), 285–302.Google Scholar
Cross Ref
Index Terms
Cumulative Quality Modeling for HTTP Adaptive Streaming






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