Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.

1. Leonardo Peroni1,2 Sergey Gorinsky1 Farzad Tashtarian 3 Christian Timmerer 3
Empowerment of Atypical Viewers
via Low-Effort Personalized Modeling
of Video Streaming Quality:
Artifacts
1IMDEA Networks Institute
2Universidad Carlos III de Madrid
Spain
1
3Alpen-Adria Universität Klagenfurt
Austria
CoNEXT, Paris, France, 5 December 2023

2. viewer
sampler modeler
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
Paper Summary
2
iQoE Experiences Scores Conclusion
Website
 Quality of Experience (QoE) in Adaptive Bitrate (ABR) video streaming
 A one-size-fits-all QoE model is not accurate for everyone
 Individualized QoE (iQoE)
• The viewer participates in the construction of a personalized model
• A new sampler and modeler interact in active learning
• The estimated effort of the viewer is around 22 minutes
subjective tests
Repository
personalized
QoE model

3.  Link to the website: https://iqoe.itec.aau.at/
• Username: anonym
• Password: iQoE_92
 120 experiences rated by the viewer
Website
3
iQoE Experiences Conclusion
Website
server
experience
set
browser
slider
player
experience
score
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
QoE
model
viewer
4 3 1
2 4
Repository
sampler
modeler
Scores

4. Experience Set
4
iQoE Experiences Conclusion
Website
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
Repository
video at the highest quality
2 minutes
…
encoded videos
2 minutes
13
represenatations
experience set
4 chunks
…
1000
experiences
Scores
…
chunkified videos
60 chunks
13
represenatations
*102 network traces and 3 ABR algorithms
…
experience influence factors
60 chunks
1000
experiences

5. Scores of Individual QoE Perception
5
iQoE Experiences Repository Conclusion
Website
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
 120 real raters
• Age from 20 to 63 years old
• 47 different countries
• Browsers: Chorme 96%, Firefox 4%
• Viewing device: personal computer 94%, phone 6%
• Screen resolutions: 29 form 360x640 to 3840x2160
 14,400 individual scores
experiences
influence factors scores
Scores

6. GitHub Repository
6
iQoE Experiences Repository Conclusion
Website
Subjective_assessments
Synthetic_raters
Datasets
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
Figure 1
Table 1
Level A (LA)
Level B (LB)
Figure 18
Table 2
Figure 5
…
…
Scores

7. Conclusion
7
iQoE Experiences Repository Conclusion
Website
 iQoE, low-effort construction of accurate personalized QoE models
 Artifacts
• Website
• Experience set
• Scores of individual QoE perception
• Repository with all data and code
 Paper presentation: Application session, Thursday, 7 December, 11:00 am
iQoE dataset and code available at:
https://github.com/Leo-rojo/iQoE_Dataset_and_Code
L. Peroni, S. Gorinsky, F. Tashtarian, and C. Timmerer, “…Personalized Modeling of Video Streaming Quality: Artifacts”
Scores