Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks

Oršolić, Irena; Seufert, Michael

Skup podataka

Fakultet elektrotehnike i računarstva, 2023. urn:nbn:hr:168:227338

Skup podataka

Skup podataka: windows-dataset.zip, 3.03 GB
Pravo pristupa: Otvoren pristup
Opis datoteke:
Real-time (per-second) QoE estimation dataset (engleski)

Skup podataka: session-dataset.csv, 48.63 MB
Pravo pristupa: Otvoren pristup
Opis datoteke:
Session (per-video) QoE estimation dataset (engleski)

Dokumentacija: windows-dataset-description.txt, 15.14 KB
Pravo pristupa: Otvoren pristup
Opis datoteke:
Dataset description for the real-time QoE estimation dataset (engleski)

Dokumentacija: session-dataset-description.txt, 10.87 KB
Pravo pristupa: Otvoren pristup
Opis datoteke:
Dataset description for the session QoE estimation dataset (engleski)

Prijavite se u repozitorij kako biste mogli spremiti objekt u svoju listu.

Citirajte ovaj rad

APA 6th Edition

Oršolić, I. i Seufert, M. (2023). Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks [Skup podataka]. https://urn.nsk.hr/urn:nbn:hr:168:227338.

MLA 8th Edition

Oršolić, Irena i Michael Seufert. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. Fakultet elektrotehnike i računarstva, 2023. 06.05.2024. https://urn.nsk.hr/urn:nbn:hr:168:227338.

Chicago 17th Edition

Oršolić, Irena, i 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.

Harvard

Oršolić, I. i Seufert, M. 2023. Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks. Fakultet elektrotehnike i računarstva. [Online]. [Citirano 06.05.2024.]. Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:168:227338.

Vancouver

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, [pristupljeno 06.05.2024.] Dostupno na: https://urn.nsk.hr/urn:nbn:hr:168:227338.

IEEE

I. Oršolić i M. Seufert, Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks, Fakultet elektrotehnike i računarstva, 2023. Citirano: 06.05.2024. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:168:227338.

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:168:227338

Naslov (engleski)	Video streaming datasets: Improving the Transfer of Machine Learning-Based Video QoE Estimation Across Diverse Networks
Autor	Irena Oršolić University of Zagreb, Faculty of Electrical Engineering and Computing
Autor	Michael Seufert University of Augsburg
Znanstveno / umjetničko područje, polje i grana	TEHNIČKE ZNANOSTI Elektrotehnika Telekomunikacije i informatika
Sažetak (engleski)	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.
Metodologija (engleski)	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.
Ključne riječi (engleski)
Jezik	engleski
URN:NBN	urn:nbn:hr:168:227338
Datum objave	2023-10-08
Verzija	1.0
Geolokacija	Zagreb, Hrvatska
Geolokacija	Würzburg, Njemačka
Projekt	Šifra: IP-2019-04-9793 Naziv (hrvatski): Modeliranje i praćenje iskustvene kvalitete imerzivnih višemedijskih usluga u 5G mrežama Naziv (engleski): Modeling and Monitoring QoE for Immersive 5G-Enabled Multimedia Services Kratica: Q-MERSIVE Voditelj: Lea Skorin-Kapov Pravna nadležnost: Hrvatska Financijer: HRZZ Linija financiranja: IP
Izdavač	Fakultet elektrotehnike i računarstva Faculty of Electrical Engineering and Computing
Prava pristupa	Otvoreni pristup
Uvjeti korištenja
Datum i vrijeme pohrane	2023-10-08 18:08:15