DATASETS AND TOOLS

All of the below listed datasets are made publicly available for research purposes. If you use the datasets, we kindly ask that you cite the relevant publication that is listed. 

To gain access to any of the below listed datasets, please send an email with your request to: muexlab@fer.hr

 

YouTube QoE/KPI classification datasets - network traffic features annotated with MOS/KPIs

1) QoMEX 2019: Two datasets involving YouTube video streaming sessions to an Android smartphone were collected during Feb. and Mar. of 2019. All traffic was transmitted using the QUIC protocol. Dataset D1 was recorded by streaming 10 different YouTube playlists (each with 10 videos) without any user interactions invoked. Dataset D2 was recorded by streaming one playlist consisting of 8 videos, and included the previously described user interactions, manually invoked during playback.

The datasets are described and analysed in the paper:

  • Ivan Bartolec, Irena Oršolić, Lea Skorin-Kapov, "In-Network YouTube Performance Estimation in Light of End User Playback-Related Interactions", to appear in 11th International Conference on Quality of Multimedia Experience, QoMEX 2019, Berlin, Germany.

Each dataset is a CSV file used to train machine learning-based models. The file contains all derived features (on a per video level) and the following target labels: longest resolution (two classes); initial delay (two classes), average video bitrate (two and three classes).

The lab set up and list of features selected for machine learning model training are provided here.

 

2) QoMEX 2018: We have collected network- and application-level data from 394 YouTube video streaming sessions on Android device connected to a WiFi network in order to train machine learning models which predict YouTube performance from network traffic features. The dataset is described and analysed in the paper:

 

  • I. Oršolić, M. Sužnjević, L. Skorin-Kapov. "YouTube QoE Estimation from Encrypted Traffic: Comparison of Test Methodologies and Machine Learning Based Models", 10th International Conference on Quality of Multimedia Experience (QoMEX 2018), Sardinia, Italy, May 2018.

 

3) CNSM 2018: A similar dataset was collected with an iOS client, described and analysed in the paper:

  • I. Oršolić, P. Rebernjak, M. Sužnjević, L. Skorin-Kapov, "In-Network QoE and KPI Monitoring of Mobile YouTube Traffic: Insights for Encrypted iOS Flows", in proc. of the 14th International Conference on Network and Service Management, CNSM 2018, Rome, Italy, Nov. 2018.

 

MMORPG QoE gaming dataset - subjective user scores of QoE, immersion, fluidity, responsiveness, immersion and challenge level

We have performed two subjective studies on MMORPG World of Warcraft gathering over 10 000 responses on various subjective metrics from 104 participants. The dataset is described and analyzed in the following papers:

  • M. Suznjevic, L. Skorin-Kapov, Aleksandra Cerekovic, Maja Matijasevic, "How to Measure and Model QoE for Networked Games?  The case study of World of Warcraft ", Multimedia Systems, Springer, 2019.
  •  M. Sužnjevic, L. Skorin-Kapov, M. Matijašević. "The Impact of User, System, and Context factors on Gaming QoE: a Case Study Involving MMORPGs", Proc. of NetGames 2013, Denver, USA, Dec. 9-10, Dec. 2013.
 
Cloud gaming dataset - PC game play video traces annotated with video metrics

We have recorded gaming sessions of 25 different video games and collected 225 different video traces. This data set is described and analyzed in the paper:

 
Cloud gaming dataset - subjective user scores of QoE, graphics quality, fluidity and willingness to play

We have performed two subjective studies on 3 different games gathering over 8 000 responses on various subjective metrics from 80 participants. The dataset is analysed and described in the paper: