Towards QoE assessment of encrypted YouTube adaptive video streaming in mobile networks
Abstract
Video streaming has become one of the most prevalent mobile applications, and takes a huge portion of the traffic on mobile networks today. YouTube is one of the most popular and volume-dominant video content providers. Understanding the user perception on the quality (i.e., Quality of Experience or QoE) of YouTube video streaming services is thus paramount for the content provider as well as its content delivery network (CDN) providers. Although various video QoE assessment approaches proposed to use different Key Performance Indicators (KPIs), they are all essentially related to a common parameter: Bitrate. However, after YouTube adopted HTTPS as its adaptive video streaming method to better protect user privacy and network security, bitrate cannot be obtained anymore from encrypted video traffic via typical deep packet inspection (DPI) method. In this paper, we tackle this challenge by proposing a machine learning based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurement. For evaluating the effectiveness of MBE, we have chosen video Mean Opinion Score (vMOS) proposed by a leading telecom vendor, as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective approach to obtain in real time the bitrate information from encrypted video streaming traffic.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Subjective evaluation is considered the most accurate method of getting a user's opinion.
“Subjective evaluation is considered as the most accurate method of getting a user's opinion.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [23]
Subjective evaluation is a complex method that fails for real-time applications.
“However, it is a complex method that fails for real-time applications [23].”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [23]
Cites
Quantification of YouTube QoE via Crowdsourcing
Hoßfeld, T., Seufert, M., Hirth, M., Zinner, T., Tran‐Gia, P., & Schatz, R. (2011) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2011.87 · Cited by 338 · see citing works
Initial delay vs. interruptions: Between the devil and the deep blue sea
Hoßfeld, T., Egger, S., Schatz, R., Fiedler, M., Masuch, K., & Lorentzen, C. (2012) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2012.6263849 · Cited by 243 · see citing works
Markov chain fingerprinting to classify encrypted traffic
Korczyński, M., & Duda, A. (2014) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2014.6848005 · Cited by 196 · see citing works
Internet traffic classification demystified
Lim, Y.-S., Kim, H.-C., Jeong, J., Kim, C.-K., Kwon, T. T., & Choi, Y. (2010) · Proceedings of the 6th International Conference · doi:10.1145/1921168.1921180 · Cited by 125 · see citing works
Aggarwal, V., Halepovic, E., Pang, J., Venkataraman, S., & Yan, H. (2014) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/2565585.2565600 · Cited by 124 · see citing works
Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming
Hoßfeld, T., Seufert, M., Sieber, C., & Zinner, T. (2014) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2014.6982305 · Cited by 117 · see citing works
YoMoApp: A tool for analyzing QoE of YouTube HTTP adaptive streaming in mobile networks
Wamser, F., Seufert, M., Casas, P., Irmer, R., Tran‐Gia, P., & Schatz, R. (2015) · European Conference on Networks and Communications · doi:10.1109/eucnc.2015.7194076 · Cited by 110 · see citing works
Casas, P., Seufert, M., & Schatz, R. (2013) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2518025.2518033 · Cited by 94 · see citing works
Characteristics of mobile youtube traffic
Ramos-Munoz, J. J., Prados-Garzon, J., Ameigeiras, P., et al. (2014) · IEEE Wireless Communications · doi:10.1109/mwc.2014.6757893 · Cited by 69 · see citing works
Empirical Evaluation of HTTP Adaptive Streaming under Vehicular Mobility
Yao, J., Kanhere, S. S., Hossain, I., & Hassan, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20757-0_8 · Cited by 61 · see citing works
Nam, H., Kim, K.-H., Calin, D., et al. (2014) · doi:10.1145/2619239.2631433 · Cited by 33 · see citing works
Nam, H., Kim, K.-H., Calin, D., et al. (2014) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2740070.2631433 · Cited by 13 · see citing works
Demonstrating the prospects of dynamic application-aware networking in a home environment
Wamser, F., Zinner, T., Iffländer, L., et al. (2014) · doi:10.1145/2619239.2631450 · Cited by 10 · see citing works
Demonstrating the prospects of dynamic application-aware networking in a home environment
Wamser, F., Zinner, T., Iffländer, L., et al. (2014) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2740070.2631450 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
QoE in IoT: a vision, survey and future directions
Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D. (2021) · Discover Internet of Things · doi:10.1007/s43926-021-00006-7 · Cited by 107 · see citing works
“However, it is a complex method that fails for real-time applications [23].”
Background
- Authors
- Pan, W., Cheng, G., Wu, H., & Tang, Y.
- Affiliations
- Ministry of Education of the People's Republic of China (CN)
- Illinois State University (US)
- Venue
- 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1-6
- ISSN
- 1548-615X (ISSN-L)
- OpenAlex
- W2534501048
- Cited by
- 30
- References
- 14
- Citations by year
- 2025: 1 · 2024: 3 · 2022: 4 · 2021: 3 · 2020: 5 · 2019: 2 · 2018: 4 · 2017: 8
- FWCI
- 1.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Workshop on Quality of Service (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- quality of experience, YouTube, adaptive video streaming, HTTPS, encrypted traffic, machine learning, mean opinion score, key performance indicators, cellular networks, content delivery networks, deep packet inspection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
