Abstract
5G promises unseen network rates and capacity. Furthermore, 5G ambitions agile networking for specific service traffic catalysing the application and network symbiosis. Nowadays, the video streaming services consume lots of networking assets and produce high dynamics caused by players mobility meaning a challenging traffic for network management. The Quality of Experience (QoE) metric defined by ITU-T P.1203 formulates the playback issues related to widely employed Dynamic Adaptive Streaming over HTTP (DASH) technologies based on a set of parameters measured at the video player. Monitoring the individual QoE is essential to dynamically provide the best experience to each user in a cell, while video players compete to enhance their individual QoE and cause high network performance dynamics. The edge systems have a perfect position to bring live coordination to dense and dynamic environments, but they are not aware of QoE experienced by each video player. This work proposes a mechanism to assess QoE scores from network dynamics at the cell and manifests of DASH streams without an explicit out of band messaging from video players to edge systems. Hence, this paper implements an edge proxy, independent from video servers and players, to monitor and estimate QoE providing the required information to later decide streaming qualities in a coordinated manner in a dense client cell. Its lightweight computation design provides real-time and distributed processing of local sessions. To check its validity, a WiFi setup has been exercised where the accuracy of the system at the edge is checked by assessing the ITU-T P.1203 QoE of individual players.
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References
Akhshabi S, Anantakrishnan L, Begen AC, Dovrolis C (2012) What happens when HTTP adaptive streaming players compete for bandwidth?. In: Proceedings of the 22nd international workshop on network and operating system support for digital audio and video, pp 9–14. https://doi.org/10.1145/2229087.2229092
Akhshabi S, Anantakrishnan L, Dovrolis C, Begen AC (2013) Server-based traffic shaping for stabilizing oscillating adaptive streaming players. In: Proceeding of the 23rd ACM workshop on network and operating systems support for digital audio and video, pp 19–24. https://doi.org/10.1145/2460782.2460786
De Cock J, Li Z, Manohara M, Aaron A (2016) Complexity-based consistent-quality encoding in the cloud. In: IEEE international conference on image processing (ICIP), pp 1484–1488. https://doi.org/10.1109/ICIP.2016.7532605
Dutta S, Taleb T, Frangoudis PA, Ksentini A (2016) On-the-fly QoE-Aware Transcoding in the Mobile Edge. In: IEEE global communications conference (GLOBECOM), pp 1–6. https://doi.org/10.1109/GLOCOM.2016.7842074
ETSI (2018) ETSI GS MEC 002 version 2.1.1 - Multi-access Edge Computing (MEC): Phase 2: Use Cases and Requirements. https://www.etsi.org/deliver/etsi_gs/MEC/001_099/002/02.01.01_60/gs_MEC002v020101p.pdf Accessed 13 Dec 2020
ETSI (2018) ETSI TS 122 261 version 15.6.0 - 5G; Service requirements for next generation new services and markets. https://www.etsi.org/deliver/etsi_ts/122200_122299/122261/15.06.00_60/ts_122261v150600p.pdf Accessed 13 Dec 2020
ETSI (2020) ETSI GS MEC 028 version 2.1.1 - Multi-access Edge Computing (MEC); WLAN Information API. https://www.etsi.org/deliver/etsi_gs/MEC/001_099/028/02.01.01_60/gs_MEC028v020101p.pdf Accessed 13 Dec 2020
Fajardo J, Taboada I, Liberal I (2015) Improving content delivery efficiency through multi-layer mobile edge adaptation. IEEE Netw 29(6):40–46. https://doi.org/10.1109/MNET.2015.7340423
Ge C, Wang N (2018) Real-time QoE estimation of DASH-based mobile video applications through edge computing. In: IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 766–771. https://doi.org/10.1109/INFCOMW.2018.8406935
Ge X, Tu S, Mao G, Wang C, Han T (2016) 5G ultra-dense cellular networks. IEEE Wirel Commun 23(1):72–79. https://doi.org/10.1109/MWC.2016.7422408
GStreamer: open source multimedia framework. https://gstreamer.freedesktop.org/ Accessed 13 December 2020
Huang W, Zhou Y, Xie X, Wu D, Chen M, Ngai E (2018) Buffer state is enough: Simplifying the design of QoE-Aware HTTP adaptive video streaming. IEEE Trans Broadcast 64(2):590–601. https://doi.org/10.1109/TBC.2018.2789580
ITU-T Rec. P.1203 Standalone Implementation. https://github.com/itu-p1203/itu-p1203 Accessed 13 Dec 2020
Juluri P, Tamarapalli V, Medhi D (2016) Measurement of quality of experience of video-on-demand services: a survey. IEEE Commun Surv Tutorials 18 (1):401–418. https://doi.org/10.1109/COMST.2015.2401424
Khan K, Goodridge W (2018) What happens when adaptive video streaming players compete in time-varying bandwidth conditions?. International journal of advanced networking and applications, pp 3704–3712. https://doi.org/10.35444/IJANA.2018.10015
Koskimies L, Taleb T, Bagaa M (2017) Qoe estimation-based server benchmarking for virtual video delivery platform. In: IEEE international conference on communications (ICC), pp 1–6. https://doi.org/10.1109/ICC.2017.7996445
Lederer S, Mueller C, Timmerer C, Concolato C, Le Feuvre J, Fliegel K (2013) Distributed DASH dataset. In: Proceedings of the 4th ACM multimedia systems conference, pp 131–135. https://doi.org/10.1145/2483977.2483994
Liotou E, Tsolkas D, Passas N (2016) A roadmap on QoE metrics and models. In: International conference on telecommunications (ICT), pp 1–5. https://doi.org/10.1109/ICT.2016.7500363
Mangla T, Halepovic E, Ammar M, Zegura E (2017) MIMIC: Using passive network measurements to estimate HTTP-based adaptive video QoE metrics. In: 2017 network traffic measurement and analysis conference, pp 1–6. https://doi.org/10.23919/TMA.2017.8002920
Mangla T, Halepovic E, Ammar M, Zegura E (2018) emimic: Estimating http-based video qoe metrics from encrypted network traffic. In: 2018 network traffic measurement and analysis conference, pp 1–8. https://doi.org/10.23919/TMA.2018.8506519
Mazhar MH, Shafiq Z (2018) Real-time video quality of experience monitoring for https and quic. In: IEEE INFOCOM 2018-IEEE conference on computer communications:, pp 1331–1339. https://doi.org/10.1109/INFOCOM.2018.8486321
Mozilla MDN web docs, User-Agent. https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/User-Agent Accessed 13 Dec 2020
Mueller C, Lederer S, Grandl R, Timmerer C (2015) Oscillation compensating dynamic adaptive streaming over http. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp 1–6. https://doi.org/10.1109/ICME.2015.7177435
Node.js: asynchronous event driven JavaScript runtime. https://nodejs.org/en/ Accessed 13 Dec 2020
Quadri C, Gaito S, Bruschi R, Davoli F, Rossi GP (2018) A MEC approach to improve QoE of video delivery service in urban spaces. In: IEEE International Conference on Smart Computing (SMARTCOMP), pp 25–32. https://doi.org/10.1109/SMARTCOMP.2018.00095
Raake A, Garcia MN, Robitza W, List P, Göring S, Feiten B (2017) A bitstream-based, scalable video-quality model for HTTP adaptive streaming: ITU-T P.1203.1. In: 2017 Ninth international conference on quality of multimedia experience (QoMEX), pp 1–6. https://doi.org/10.1109/QoMEX.2017.7965631
Recommendation ITU-T P.800.1 (2016) Mean opinion score (MOS) terminology
Recommendation ITU-T P.1203 (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport
Richardson M, Wallace S (2012) Getting started with raspberry PI. O’Reilly Media Inc
Robitza W, Göring S, Raake A, Lindegren D, Heikkilä G, Gustafsson J, List P, Feiten B, Wüstenhagen U, Garcia MN, Yamagishi K, Broom S (2018) HTTP Adaptive Streaming QoE Estimation With ITU-t Rec. In: P.1203 – Open Databases and Software, 9th ACM Multimedia Systems Conference, pp 466–471. https://doi.org/10.1145/3204949.3208124
Younis A, Tran TX, Pompili D (2019) On-Demand Video-Streaming Quality of experience maximization in mobile edge computing. IEEE international symposium on ”A world of wireless, mobile and multimedia networks, (WoWMoM), pp 1–9. https://doi.org/10.1109/WoWMoM.2019.8793052
Yu H, Zheng D, Zhao BJ, Zheng W (2006) Understanding user behavior in large-scale video-on-demand systems. ACM SIGOPS Operating Systems Review 40(4):333–344. https://doi.org/10.1145/1218063.1217968
Zhang X, Wang J (2018) Joint heterogeneous statistical-QoS/QoE provisionings for edge-computing based WiFi offloading over 5G mobile wireless networks. In: Annual conference on information sciences and systems (CISS), pp 1–6. https://doi.org/10.1109/CISS.2018.8362265
Acknowledgements
This work was fully supported by the 5G-TEST project (Gipuzkoa’s research and innovation programme) and Open-VERSO project (Red Cervera program, Spanish government’s Centre for the Development of Industrial Technology).
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Viola, R., Zorrilla, M., Angueira, P. et al. Multi-access Edge Computing video analytics of ITU-T P.1203 Quality of Experience for streaming monitoring in dense client cells. Multimed Tools Appl 81, 12387–12403 (2022). https://doi.org/10.1007/s11042-022-12537-4
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DOI: https://doi.org/10.1007/s11042-022-12537-4