Documentation: windows-dataset-description.txt, 15.14 KB Access Condition: Open access Description: Dataset description for the real-time QoE estimation dataset (English)
Documentation: session-dataset-description.txt, 10.87 KB Access Condition: Open access Description: Dataset description for the session QoE estimation dataset (English)
Please login to the repository to save this object to your list.
Cite this document
Oršolić, I. & Seufert, M. (2023). Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks [Data set]. https://urn.nsk.hr/urn:nbn:hr:168:227338.
Oršolić, Irena and Michael Seufert. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. Fakultet elektrotehnike i računarstva, 2023. 27 Oct 2024. https://urn.nsk.hr/urn:nbn:hr:168:227338.
Oršolić, Irena, and Michael Seufert. 2023. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. Fakultet elektrotehnike i računarstva. https://urn.nsk.hr/urn:nbn:hr:168:227338.
Oršolić, I. and Seufert, M. 2023. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. Fakultet elektrotehnike i računarstva. [Online]. [Accessed 27 October 2024]. Available from: https://urn.nsk.hr/urn:nbn:hr:168:227338.
Oršolić I, Seufert M. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. [Internet]. Fakultet elektrotehnike i računarstva: Zagreb, HR; 2023, [cited 2024 October 27] Available from: https://urn.nsk.hr/urn:nbn:hr:168:227338.
I. Oršolić and M. Seufert, Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks, Fakultet elektrotehnike i računarstva, 2023. Accessed on: Oct 27, 2024. Available: https://urn.nsk.hr/urn:nbn:hr:168:227338.
Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks
Author
Irena Oršolić University of Zagreb, Faculty of Electrical Engineering and Computing
Author
Michael Seufert University of Augsburg
Scientific / art field, discipline and subdiscipline
TECHNICAL SCIENCES Electrical Engineering Telecommunications and Informatics
Abstract (english)
The datasets in this repository consist of video on demand streaming data collected at two locations (Würzburg, Germany and Zagreb, Croatia) and across two years (2020 and 2021). We refer to the datasets by using the following labels: Wue_2020, Wue_2021, Zag_2020, Zag_2021. The data includes network traffic features used to estimate Quality of Experience (QoE) and Key Performance Indicators (KPI) of video streaming sessions using machine learning. The traffic features are annotated with QoE/KPI classes, with samples considered both on a session-level (per-video) and in real-time fashion (per-second). The datasets are collected for and presented in the journal article entitled "Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks", authored by Michael Seufert and Irena Oršolić, published in IEEE Transactions on Network and Service Management in 2023.
Methods (english)
The measurements were conducted using a browser automation tool, that initiated the streaming of predefined videos from a popular video streaming service. The set of 2000 distinct videos was streamed to a laptop at two different locations, both in 2020 and 2021, with and without using an ad-blocking plugin, under 3 different bandwidth constraints (unlimited, 1Mbps, and stochastic). Given all the combinations of the conditions, this results in 48000 streamed videos. After eliminating log inconsistencies, the dataset published in this repository includes 8833 videos from Wue_2020, 9410 from Zag_2020, 5310 from Wue_2021, and 6640 from Zag_2021.