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Proposal of a tool for the automatic estimation of quality of experience in MPEG-DASH scenarios

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Warianty tytułu
PL
Propozycja narzędzia do automatycznej oceny jakości doświadczeń w scenariuszach MPEG-DASH
Języki publikacji
EN
Abstrakty
EN
Service providers must be able to identify the quality perceived by users through data networks, which is the quality of experience (QoE) required to take the necessary actions to provide a service that meets the user’s expectations. Therefore, this article proposes a tool for estimating the subjective QoE from objective parameters of the quality of service (QoS) for the video streaming supported by MPEG-DASH (Moving Picture Experts Group- Dynamic Adaptive Strea.
PL
Dostawcy usług muszą być w stanie określić jakość postrzeganą przez użytkowników za pośrednictwem sieci danych, czyli jakość doświadczenia (QoE) wymaganą do podjęcia niezbędnych działań w celu świadczenia usługi spełniającej oczekiwania użytkownika. Dlatego w niniejszym artykule zaproponowano narzędzie do szacowania subiektywnego QoE na podstawie obiektywnych parametrów jakości usług (QoS) dla strumieniowego przesyłania wideo obsługiwanego przez MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP). W przyszłych pracach proponujemy ekstrapolację opracowanego narzędzia na usługi wideo na żądanie w różnych kontekstach aplikacji.
Rocznik
Strony
139--145
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Faculty of Engineering, University of Quindío – Colombia
  • Faculty of Engineering, University of Quindío – Colombia
  • University of Cartagena, Campus Piedra de Bolívar, Cartagena, Bolívar, Colombia
Bibliografia
  • [1] L. Castañeda_Herrera, A. Duque-Torres, and W. Y. Campo_Muñoz, “View of An Approach Based on Knowledge-Defined Networking for Identifying Video streaming Flows in 5G Networks.,” IEEE Lat. Am., vol. 19, pp. 1737–1774, 2021.
  • [2] N. Eswara et al., “Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 30, no. 3, pp. 661–673, Mar. 2020, doi: 10.1109/TCSVT.2019.2895223.
  • [3] N. Eswara et al., “A Continuous QoE Evaluation Framework for Video Streaming over HTTP,” IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 11, pp. 3236–3250, 2017, doi: 10.1109/TCSVT.2017.2742601.
  • [4] D. Ghadiyaram, J. Pan, and A. C. Bovik, “A Subjective and Objective Study of Stalling Events in Mobile Streaming Videos,”IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 1, pp. 183–197, 2019, doi: 10.1109/TCSVT.2017.2768542.
  • [5] S. Fan and H. Zhao, “Delay-based cross-layer QoS scheme for video streaming in wireless ad hoc networks,” China Commun., vol. 15, no. 9, pp. 215–234, Sep. 2018, doi: 10.1109/CC.2018.8456464.
  • [6] Cisco, “Cisco Visual Networking Index: Forecast and Trends, 2017–2022,” 2019.
  • [7] Ericsson, “Ericsson Mobility Report November 2020,” 2020. https://www.ericsson.com/4adc87/assets/local/mobility-report/documents/2020/november-2020-ericsson-mobility-report.pdf.
  • [8] M. Elrotub and A. Gherbi, “Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment,” Int. J. Softw. Sci. Comput. Intell., vol. 13, no. 2, p. 28, 2021, doi: 10.4018/IJSSCI.2021040103.
  • [9] A. M. Manasrah, A. Aldomi, and B. B. Gupta, “An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment,” Clust. Comput. 2017 221, vol. 22, no. 1, pp. 1639–1653, Dec. 2017, doi: 10.1007/S10586-017-1559-Z.
  • [10] S. P. Ahuja, E. Czarnecki, and S. Willison, “Multi-Factor Performance Comparison of Amazon Web Services Elastic Compute Cluster and Google Cloud Platform Compute Engine,” Int. J. Cloud Appl. Comput., vol. 10, no. 3, p. 16, 2020, doi: 10.4018/IJCAC.2020070101.
  • [11] “ITU-T How to increase QoS/QoE of IP-based platform(s) to regionally agreed standards,” 2013.
  • [12] N. Banovic-Curguz and D. Iliševic, “Mapping of QoS/QoE in 5G networks,” 2019 42nd Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2019 - Proc., pp. 404–408, May 2019, doi: 10.23919/MIPRO.2019.8757034.
  • [13] P. Uthansakul, P. Anchuen, M. Uthansakul, and A. Ahmad Khan, “Estimating and Synthesizing QoE Based on QoS Measurement for Improving Multimedia Services on Cellular Networks Using ANN Method,” IEEE Trans. Netw. Serv. Manag., vol. 17, no. 1, pp. 389–402, Mar. 2020, doi: 10.1109/TNSM.2019.2946091.
  • [14] ITU-T, “G.107 : The E-model: a computational model for use in transmission planning.” p. 30, 2015.
  • [15] M. Alreshoodi and J. Woods, “Survery on QoE/QoS correlation models for multimedia services,” Int. J. Distrib. Parallel Syst. (IJDPS, vol. 4, no. 3, 2013, doi: 10.5121/ijdps.2013.4305.
  • [16] ITU-T, “P.1203 : Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport.” p. 22, 2017.
  • [17] W. E. Shabrina, D. Wisaksono Sudiharto, E. Ariyanto, and M. Al Makky, “The QoS Improvement Using CDN for Live Video Streaming with HLS,” Feb. 2020, doi: 10.1109/ICoSTA48221.2020.1570613984.
  • [18] J. Moya Neyra, C. Alonso Irizar, and C. Anias Calderón, “Evaluación de QoE en servicios IP basada en parámetros de QoS,” Ing. Electrónica, Automática y Comun., vol. 38, no. 3, 2017.
  • [19] F. Ribeiro, D. Florêncio, C. Zhang, and M. Seltzer, “CROWDMOS: An approach for crowdsourcing mean opinion score studies,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2011, pp. 2416–2419, doi: 10.1109/ICASSP.2011.5946971.
  • [20] Y. Gao, X. Wei, and L. Zhou, “Personalized QoE Improvement for Networking Video Service,” IEEE J. Sel. Areas Commun., vol. 38, no. 10, pp. 2311–2323, Oct. 2020, doi: 10.1109/JSAC.2020.3000395.
  • [21] P. T. A. Quang, K. Piamrat, K. D. Singh, and C. Viho, “Video Streaming over Ad Hoc Networks: A QoE-Based Optimal Routing Solution,” IEEE Trans. Veh. Technol., vol. 66, no. 2, pp. 1533–1546, Feb. 2017, doi: 10.1109/TVT.2016.2552041.
  • [22] A. Raake et al., “Multi-Model Standard for Bitstream-, Pixel-Based and Hybrid Video Quality Assessment of UHD/4K: ITU-T P.1204,” IEEE Access, vol. 8, pp. 193020–193049, 2020, doi: 10.1109/ACCESS.2020.3032080.
  • [23] M. Deressa, M. Sheng, M. Wimmers, J. Liu, and M. Mekonnen, “Maximizing Quality of Experience in Device-to-Device Communication Using an Evolutionary Algorithm Based on Users’ Behavior,” IEEE Access, vol. 5, pp. 3878–3888, 2017, doi: 10.1109/ACCESS.2017.2685420.
  • [24] Y. Hao, J. Yang, M. Chen, M. S. Hossain, and M. F. Alhamid, “Emotion-Aware Video QoE Assessment Via Transfer Learning,” IEEE Multimed., vol. 26, no. 1, pp. 31–40, Jan. 2019, doi: 10.1109/MMUL.2018.2879590.
  • [25] L. Amour, M. I. Boulabiar, S. Souihi, and A. Mellouk, “An improved QoE estimation method based on QoS and affective computing,” Proc. 2018 13th Int. Symp. Program. Syst. ISPS 2018, pp. 1–6, Jun. 2018, doi: 10.1109/ISPS.2018.8379009.
  • [26] W. Robitza et al., “HTTP adaptive streaming QoE estimation with ITU-T rec. P.1203 - Open databases and software,” Proc. 9th ACM Multimed. Syst. Conf. MMSys 2018, pp. 466–471, 2018, doi: 10.1145/3204949.3208124.
  • [27] H. F. Bermudez, J. M. Martinez-Caro, R. Sanchez-Iborra, J. L. Arciniegas, and M. D. Cano, “Live video-streaming evaluation using the ITU-T P.1203 QoE model in LTE networks,” Comput. Networks, vol. 165, p. 106967, 2019, doi: 10.1016/j.comnet.2019.106967.
  • [28] P. G. Castillo, P. A. Vila, and J. C. G. Cebollada, “AutomaticQoE evaluation of DASH streaming using ITU-T standard P.1203 and google puppeteer,” in PE-WASUN 2019 - Proceedings of the 16th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, Nov. 2019, pp. 79–86, doi: 10.1145/3345860.3361519.
  • [29] ITU-T, “Recommendation ITU-T P.910,” Subj. video Qual. Assess. methods Multimed. Appl., 2008.
  • [30] A. Raake, M. N. Garcia, W. Robitza, P. List, S. Göring, and B.Feiten, “A bitstream-based, scalable video-quality model for HTTP adaptive streaming: ITU-T P.1203.1,” in 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017, May 2017, vol. 12, pp. 1–6, doi: 10.1109/QoMEX.2017.7965631.
  • [31] X. Che, B. Ip, and L. Lin, “A Survey of Current YouTube Video Characteristics,” IEEE Multimed., vol. 22, no. 2, pp. 56–63, 2015, doi: 10.1109/MMUL.2015.34.
  • [32] N. Barman and M. G. Martini, “QoE Modeling for HTTP Adaptive Video Streaming-A Survey and Open Challenges,” IEEE Access, vol. 7, no. c, pp. 30831–30859, 2019, doi: 10.1109/ACCESS.2019.2901778
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7bfe283c-db81-4e0d-9749-c6f6625d4516
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