Mostrar el registro sencillo del ítem
dc.contributor.author | Taha, Miran![]() |
es_ES |
dc.contributor.author | Ali, Aree![]() |
es_ES |
dc.contributor.author | Lloret, Jaime![]() |
es_ES |
dc.contributor.author | Gondim, Paulo R. L.![]() |
es_ES |
dc.contributor.author | Canovas, Alejandro![]() |
es_ES |
dc.date.accessioned | 2023-09-21T18:04:44Z | |
dc.date.available | 2023-09-21T18:04:44Z | |
dc.date.issued | 2021-07 | es_ES |
dc.identifier.issn | 1380-7501 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/196897 | |
dc.description.abstract | [EN] Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client's application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment's evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user's experience. The automated model could function to demonstrate the importance of correlation for network behaviors' parameters. Consequently, it directly influences the satisfaction of the end-user's perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples. | es_ES |
dc.description.sponsorship | This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | Multimedia Tools and Applications | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | HTTP adaptive streaming | es_ES |
dc.subject | Correlation coefficient | es_ES |
dc.subject | QoE | es_ES |
dc.subject | QoS | es_ES |
dc.subject | Subjective methodology | es_ES |
dc.subject.classification | INGENIERÍA TELEMÁTICA | es_ES |
dc.title | An automated model for the assessment of QoE of adaptive video streaming over wireless networks | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s11042-021-10934-9 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84802-C2-1-P/ES/RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Taha, M.; Ali, A.; Lloret, J.; Gondim, PRL.; Canovas, A. (2021). An automated model for the assessment of QoE of adaptive video streaming over wireless networks. Multimedia Tools and Applications. 80(17):26833-26854. https://doi.org/10.1007/s11042-021-10934-9 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s11042-021-10934-9 | es_ES |
dc.description.upvformatpinicio | 26833 | es_ES |
dc.description.upvformatpfin | 26854 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 80 | es_ES |
dc.description.issue | 17 | es_ES |
dc.relation.pasarela | S\473268 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.description.references | Abar T, Letaifa AB, Elasmi S (2018) Enhancing QoE based on machine learning and DASH in SDN networks. 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), IEEE, Krakow, Poland, pp. 258–263, https://doi.org/10.1109/WAINA.2018.00095 | es_ES |
dc.description.references | Absolute Category Rating (ACR) ITU-R BT.500–12. Recommendation ITU-R BT.500-13Methodology for the subjective assessment of the quality of television pictures (Jan. 2012), [Accessed November 2019 Online] | es_ES |
dc.description.references | Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming–a survey and open challenges. IEEE Access 7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778 | es_ES |
dc.description.references | Barman N, Zadtootaghaj S, Schmidt S, Martini MG, Möller S (2018) An objective and subjective quality assessment study of passive gaming video streaming. International Journal of Network Management, pp. e2054. DOI: https://doi.org/10.1002/nem.2054 | es_ES |
dc.description.references | Barman N, Jammeh E, Ghorashi SA, Martini MG (2019) No-reference video quality estimation based on machine learning for passive gaming video streaming applications. IEEE Access 7:74511–74527. https://doi.org/10.1109/ACCESS.2019.2920477 | es_ES |
dc.description.references | Bulkan U, Dagiuklas T (2019) Predicting quality of experience for online video service provisioning. Multimed Tools Appl 78(13):18787–18811. https://doi.org/10.1007/s11042-019-7164-9 | es_ES |
dc.description.references | Chen Y, Wu K, Zhang Q (2014) From QoS to QoE: a tutorial on video quality assessment. IEEE Commun Surveys Tutorials 17(2):1126–1165. https://doi.org/10.1109/COMST.2014.2363139 | es_ES |
dc.description.references | Chikkerur S, Sundaram V, Reisslein M, Karam LJ (2011) Objective video quality assessment methods: a classification, review, and performance comparison. IEEE Trans Broadcast 57(2):165–182. https://doi.org/10.1109/TBC.2011.2104671 | es_ES |
dc.description.references | Cisco report, [Accessed November 2019 Online]: http://bit.ly/2gg1F6B | es_ES |
dc.description.references | Claeys M, Latre S, Famaey J, De Turck F (2014) Design and evaluation of a self-learning HTTP adaptive video streaming client. IEEE Commun Lett 18(4):716–719. https://doi.org/10.1109/LCOMM.2014.020414.132649 | es_ES |
dc.description.references | Cofano G, De Cicco L, Zinner T, Nguyen-Ngoc A, Tran-Gia P, Mascolo S (2016) Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming. In Proceedings of the 7th International Conference on Multimedia Systems, pp. 1–12. https://doi.org/10.1145/2910017.2910597 | es_ES |
dc.description.references | Duanmu Z, Zeng K, Ma K, Rehman A, Wang Z (2016) A quality-of-experience index for streaming video. IEEE J Selected Topics Sign Process 11(1):154–166. https://doi.org/10.1109/10.1109/JSTSP.2016.2608329 | es_ES |
dc.description.references | García L, Lloret J, Turro C, Taha M (2016) QoE assesment of MPEG-DASH in polimedia e-learning system. International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp. 1117–1123. DOI: https://doi.org/10.1109/ICACCI.2016.7732194 | es_ES |
dc.description.references | García B, López-Fernández L, Gortázar F, Gallego M (2019) Practical Evaluation of VMAF Perceptual Video Quality for WebRTC Aepplications. Journal of Electronics, vol. 9, no. 2. https://doi.org/10.3390/electronics8080854 | es_ES |
dc.description.references | Guan-MingSu XS, Bai Y, Wang M, Vasilakos AV, Wang H (2016) QoE in video streaming over wireless networks: perspectives and research challenges. Wirel Netw 22(5):1571–1593. https://doi.org/10.1007/s11276-015-1028-7 | es_ES |
dc.description.references | Gutterman C, Guo K, Arora S, Wang X, Wu L, Katz-Bassett E, Zussman G (2019) Requet: Real-time QoE detection for encrypted YouTube traffic. In Proceedings of the 10th ACM Multimedia Systems Conference, pp. 48–59. https://doi.org/10.1145/3304109.3306226 | es_ES |
dc.description.references | Huawei report, [Accessed December 2019 Online]: https://www.huawei.com/minisite/hwmbbf15/img/video_coverage_whitepaper_en.pdf. | es_ES |
dc.description.references | ITU-T Rec. P.1203. (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport. ITU-T Rec. P.1203 | es_ES |
dc.description.references | Juluri P, Tamarapalli V, Medhi D (2015) Measurement of quality of experience of video-on-demand services: a survey. IEEE Commun Surveys Tutorials 18(1):401–418. https://doi.org/10.1109/COMST.2015.2401424 | es_ES |
dc.description.references | Juluri P, Tamarapalli V, Medhi D (2016) Measurement of quality of experience of video-on-demand services: a survey. IEEE Commun Surveys Tutorials 18(1):401–418. https://doi.org/10.1109/COMST.2015.2401424 | es_ES |
dc.description.references | Liu X, Nan Z, Richard Yu F, Chen Y, Tang J, Leung VCM (2018) Cooperative video transmission strategies via caching in small-cell networks. IEEE Trans Veh Technol 67(12):12204–12217. https://doi.org/10.1109/TVT.2018.2874258 | es_ES |
dc.description.references | MingfuLi C-LY, Shao-Yu L (2018) Real-time QoE monitoring system for video streaming services with adaptive media playout. Int J Digital Multimed Broadcast 2018:1–11. https://doi.org/10.1155/2018/2619438 | es_ES |
dc.description.references | Mok R, Luo X, Chan E, Chang R (2012) QDASH: A QoE-aware DASH system. Proceedings of the 3rd Multimedia Systems Conference, pp. 11–22. https://doi.org/10.1145/2155555.2155558 | es_ES |
dc.description.references | Moorthy AK, Seshadrinathan K, Soundararajan R, Bovik AC (2010) Wireless video quality assessment: a study of subjective scores and objective algorithms. IEEE trans Circuits Syst Vid Technol 20(4):587–599. https://doi.org/10.1109/TCSVT.2010.2041829 | es_ES |
dc.description.references | Nam H, Kim K-H, Schulzrinne H (2016) QoE matters more than QoS: Why people stop watching cat videos. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE, pp. 1–9. https://doi.org/10.1109/INFOCOM.2016.7524426 | es_ES |
dc.description.references | Nunome T, Mizutani K (2019) The joint effect of wireless LAN reliable groupcast and a rate-adaptation mechanism on QoE of audio and video transmission. in Proc. IEEE 26th International Conference on Telecommunications (ICT), IEEE, pp. 149–153. https://doi.org/10.1109/ICT.2019.8798643 | es_ES |
dc.description.references | Nunome T, Tani H (2017) The effect of seeking operation on QoE of HTTP adaptive streaming services. Int J Comput Netw Commun (IJCNC) 9(2):1–18. https://doi.org/10.5121/ijcnc.2017.9201 | es_ES |
dc.description.references | Orsolic I, Pevec D, Suznjevic M, Skorin-Kapov L (2017) A machine learning approach to classifying YouTube QoE based on encrypted network traffics. Multimed Tools Appl 76(21):22267–22301. https://doi.org/10.1007/s11042-017-4728-4 | es_ES |
dc.description.references | Pal D, Vanijja V (2017) Effect of network QoS on user QoE for a mobile video streaming service using H. 265/VP9 codec. Proced Comput Sci 111:214–222. https://doi.org/10.1016/j.procs.2017.06.056 | es_ES |
dc.description.references | Petrangeli S, Van Der Hooft J, Wauters T, De Turck F (2018) Quality of experience-centric management of adaptive video streaming services: status and challenges. ACM Trans Multimed Comput Commun Appl (TOMM) 14(2):1–29. https://doi.org/10.1145/3165266 | es_ES |
dc.description.references | Poojary S, El-Azouzi R, Altman E, Sunny A, Triki I, Haddad M, Jimenez T, Valentin S, Tsilimantos D (2018) Analysis of QoE for adaptive video streaming over wireless networks. 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), IEEE, pp. 1–8, https://doi.org/10.23919/WIOPT.2018.8362846 | es_ES |
dc.description.references | Retal S, Bagaa M, Taleb T, Flinck H (2017) Content delivery network slicing: QoE and cost awareness. IEEE International Conference on Communications (ICC), IEEE, pp. 1–6, https://doi.org/10.1109/ICC.2017.7996499 | es_ES |
dc.description.references | Rodrigues F, Sousa I, Queluz MP, Rodrigues A (2018) QoE-aware scheduling algorithm for adaptive HTTP video delivery in wireless networks. Wireless Communications and Mobile computing, vol. 2018. DIO: https://doi.org/10.1155/2018/9736360 | es_ES |
dc.description.references | Schatz R, Sackl A, Timmerer C, Gardlo B (2017) Towards subjective quality of experience assessment for omnidirectional video streaming." Ninth International Conference on Quality of Multimedia Experience (QoMEX), IEEE, no. 6, pp. 1-, https://doi.org/10.1109/QoMEX.2017.7965657 | es_ES |
dc.description.references | Seufert M, Egger S, Slanina M, Zinner T, Hoßfeld T, Tran-Gia P (2014) A survey on quality of experience of HTTP adaptive streaming. IEEE Commun Surveys Tutorials 17(1):469–492. https://doi.org/10.1109/COMST.2014.2360940 | es_ES |
dc.description.references | Singh S, Andrews JG, de Veciana G (2012) Interference shaping for improved quality of experience for real-time video streaming. IEEE J Selected Areas Commun 30(7):1259–1269. https://doi.org/10.1109/JSAC.2012.120811 | es_ES |
dc.description.references | Stensen JMG (2012) Evaluating QoS and QoE Dimensions in Adaptive Video Streaming. Master's thesis, Institutt for telematikk, https://doi.org/10.12142/ZTECOM.201901004 | es_ES |
dc.description.references | Streijl RC, Winkler S, Hands DS (2016) Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimedia Systems 22(2):213–227. https://doi.org/10.1007/s00530-014-0446-1 | es_ES |
dc.description.references | Taha M (2016) A novel CDN testbed for fast deploying HTTP adaptive video streaming. Proceedings of the 9th EAI International Conference on Mobile Multimedia Communications, pp. 65–71. DOI: https://doi.org/10.4108/eai.18-6-2016.2264163 | es_ES |
dc.description.references | Taha M, Lloret J, Ali A, Garcia L (2018) Adaptive video streaming testbed design for performance study and assessment of QoE. Int J Commun Syst 31(9):e3551. https://doi.org/10.1002/dac.3551 | es_ES |
dc.description.references | Thang TC, Le HT, Nguyen HX, Pham AT, Kang JW, Ro YM (2013) Adaptive video streaming over HTTP with dynamic resource estimation. J Commun Netw 15(6):635–644. https://doi.org/10.1109/JCN.2013.000112 | es_ES |
dc.description.references | Timmerer C, Zabrovskiy A (2019) Automating QoS and QoE Evaluation of HTTP Adaptive Streaming Systems. ZTE COMMUNICATIONS, vol. 17, no. 1 | es_ES |
dc.description.references | Trakas P, Adelantado F, Zorba N, Verikoukis C (2017) A quality of experience-aware association algorithm for 5G heterogeneous networks. In 2017 IEEE International Conference on Communications (ICC), IEEE, pp. 1–6. DOI: https://doi.org/10.1109/ICC.2017.7996869 | es_ES |
dc.description.references | Villa BJ, Heegaard PE (2012) Improving Fairness in QoS and QoE domains for Adaptive Video Streaming. International Journal on Advances in Networks and Services vol. 5, no. 3, pp. 4 | es_ES |
dc.description.references | Qingyong Wang, Hong-Ning Dai, Di Wu, and Hong Xiao (2018) Data analysis on video streaming QoE over mobile networks. Eurasip J Wireless Commun Netw, no. 1, pp.173, https://doi.org/10.1186/s13638-018-1180-8 | es_ES |
dc.description.references | Wenjing L, Yu P, Wang R, Lei F, Dong O, Xuesong Q (2017) Quality of experience evaluation of HTTP video streaming based on user interactive behaviors. J China Univ Posts Telecommun 24(3):24–32. https://doi.org/10.1016/S1005-8885(17)60208-5 | es_ES |
dc.description.references | Xu Y, Zhou Y, Chiu D-M (2014) Analytical QoE models for bit-rate switching in dynamic adaptive streaming systems. IEEE Trans Mob Comput 13(12):2734–2748. https://doi.org/10.1109/TMC.2014.2307323 | es_ES |
dc.description.references | Zeng H, Fang Y (2013) Implementation of video transcoding client based on FFMPEG. In Advanced Materials Research, vol. 756, Trans Tech Publications Ltd, pp. 1748–1752. 10.4028/www.scientific.net/AMR.756-759.1748 | es_ES |
dc.description.references | Zhang W, Wen Y, Chen Z, Khisti A (2013) QoE-driven cache management for HTTP adaptive bit rate streaming over wireless networks. IEEE Trans Multimed 15(6):1431–1445. https://doi.org/10.1109/TMM.2013.2247583 | es_ES |
dc.description.references | Zhao T, Liu Q, Chen CW (2016) QoE in video transmission: a user experience-driven strategy. IEEE Commun Surveys Tutorials 19(1):285–302. https://doi.org/10.1109/COMST.2016.2619982 | es_ES |