Abstract
Nowadays, the widely used end-to-end encryptions have prevented the Deep Packet Inspect (DPI) method applied in Quality of Experience (QoE) prediction and optimization. Thus, for the Internet Service Providers (ISPs), it has become more difficult to monitor and improve user QoE when users are watching videos or using other video application services. To overcome the above problem, a Cross-layer Predicting framework of Video Streaming QoE (CPVS) is proposed to monitor and predict video indicators that affect user QoE, such as startup delay, stalling rate and stalling events. The most prominent advantage of the CPVS is that it can predict the video QoE indicators without the prior information of transmission protocols, whether it is encrypted or not. Furthermore, the general framework, CPVS, shows high predicting accuracy both in a time granularity as small as one second and in video-session prediction, even can perform well with only 6 features. For the session-based prediction, the best weighted f1 score achieves 85.71% about the startup delay prediction, and achieves 84.64% about the stalling rate classification. For the real-time prediction, the best weighted f1 score can achieve 79.25%. Besides, by exploring the 4G network features, we find out the 6 most critical ones that not only make the CPVS retain high accuracy but also save 35.02% running time to improve the real-time prediction performance.
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Casas P, Seufert M, Schatz R (2013) Youqmon: A system for on-line monitoring of youtube qoe in operational 3g networks. Acm Sigmetrics Performance Evaluation Review 41(2):44–46
Dimopoulos G, Leontiadis I, Barlet-Ros P, Papagiannaki K (2016) Measuring video QoE from encrypted traffic. ACM IMC:513-526 https://doi.org/10.1145/2987443.2987459
Orsolic I, Pevec D, Suznjevic M, Skorin-Kapov L (2017) A machine learning approach to classifying youtube qoe based on encrypted network traffic. Multimedia Tools and Applications 76(21):22267–22301
Aggarwal V, Halepovic E, Pang J, Venkataraman S and Yan H (2014) Prometheus: toward quality-of-experience estimation for mobile apps from passive network measurements. ACM HotMobile:1-6 https://doi.org/10.1145/2565585.2565600
Wright C V, Ballard L, Monrose F, Masson G M (2007) Language identification of encrypted VoIP traffic: Alejandra y Roberto or Alice and Bob? USENIX Security Symposium, August 2007: 6-10. https://www.usenix.org/conference/16th-usenix-security-symposium/language-identification-encrypted-voip-traffic-alejandra-y
Szilagyi P, Vulkan C (2015) Network side lightweight and scalable YouTube QoE estimation. IEEE International Conference on Communications. IEEE ICC 8-12 June 2015:3100-3106. https://ieeexplore.ieee.org/document/7248800
Eswara N, Ashique S, Panchbhai A, Chakraborty S, Channappayya SS (2020) Streaming video qoe modeling and prediction: a long short-term memory approach. IEEE Transactions on Circuits and Systems for Video Technology 30(3):661–673
Mazhar M H, Shafiq Z (2018) Real-time video quality of experience monitoring for HTTPS and QUIC. IEEE INFOCOM 16-19 April 2018:1331-1339. https://ieeexplore.ieee.org/document/8486321
Seufert M, Casas P, Wehner N, Gang L and Li K (2019) Features that matter: feature selection for on-line stalling prediction in encrypted video streaming. INFOCOM WKSHPS 29 April-2 May 2019. https://ieeexplore.ieee.org/document/8845109
Staehle B, Hirth M, Pries R, Wamser, F, Staehle D (2010) YoMo: A YouTube application comfort monitoring tool. New Dimensions in the Assessment and Support of Quality of Experience for Multimedia Applications
Casas P, Seufert M, Wamser F, Gardlo B, Sackl A, Schatz R (2016) Next to you: monitoring quality of experience in cellular networks from the end-devices. IEEE Transactions on Network Service Management 13(2):181–196
Casas P, D”Alconzo A, Wamser F, Seufert M, Gardlo B, Schwind A, et al. (2017) Predicting QoE in cellular networks using machine learning and in-smartphone measurements. IEEE Ninth International Conference on Quality of Multimedia Experience 31 May-2 June 2017:1-6. https://ieeexplore.ieee.org/document/7965687. Accessed 3 July 2017
Orsolic I, Pevec D, Suznjevic M, Skorin-Kapov L (2016) YouTube QoE estimation based on the analysis of encrypted network traffic using machine learning. 2016 IEEE Globecom Workshops 4-8 December 2016. https://ieeexplore.ieee.org/document/7849088
Seufert M, Egger S, Slanina M, Zinner T, Hobfeld T, Tran-Gia P (2017) A survey on quality of experience of http adaptive streaming. IEEE Communications Surveys Tutorials 17(1):469–492
Ghadiyaram D, Pan J, Bovik A C (2015) A time-varying subjective quality model for mobile streaming videos with stalling events. SPIE Optical Engineering + Applications:911-918
Zeng K, Yeganeh H, Wang Z (2016) Quality-of-experience of streaming video: Interactions between presentation quality and playback stalling. IEEE International Conference on Image Processing:2405-2409
Dierks T, Rescorla E (2008) The transport layer security (tls) protocol version 1.2. IETF RFC 5246. http://www.ietf.org/rfc/rfc5246.txt. Accessed Aug 2007
Cui Y, Li T, Liu C, Wang X, Kuhlewind M (2017) Innovating transport with quic: design approaches and research challenges. IEEE Internet Computing 21(2):72–76
Pantos R and May E W (2011) Http live streaming. IETF Internet draft, work in progress
Ghadiyaram D, Pan J, Bovik AC (2017) A subjective and objective study of stalling events in mobile streaming videos. IEEE Transactions on Circuits and Systems for Video Technology 29(1):183–197
Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. (2014) Learning comparison of phrase representations using rnn encoder-decoder for statistical machine translation. arXiv:1406.1078
Breiman L (2001) Random Forests. Machine Learning 45(1):5–32
Liudmila P,Gusev G ,Vorobev A, Dorogush A V, Gulin A (2018) CatBoost: unbiased boosting with categorical features. NeurIPS: 6639-6649
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. arXiv:1806.11248
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye QW, and Liu T Y (2017) LightGBM: a highly efficient gradient boosting decision tree. NIPS:3149-3157
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The work is supported in part by Guangdong Provincial Special Fund For Modern Agriculture Industry Technology Innovation Teams (No. 2021KJ122), in part by the State’s Key Project of Research and Development Plan under Grants (2019YFE0196400), in part by the Guangdong R&D Project in Key Areas under Grant (2019B010158001), in part by the Industry-University-Research Cooperation Project in Zhuhai (No. ZH22017001200072PWC) and in part by the Open Research Fund Program of Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University.
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Chen, J., Mai, W., Lian, X. et al. Ignoring Encrypted Protocols: Cross-layer Prediction of Video Streaming QoE Metrics. Mobile Netw Appl 27, 2459–2468 (2022). https://doi.org/10.1007/s11036-021-01890-7
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DOI: https://doi.org/10.1007/s11036-021-01890-7