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

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 "YouTube QoE Estimation from Encrypted Traffic: Comparison of Test Methodologies and Machine Learning Based Models", 10th International Conference on Quality of Multimedia Experience (QoMEX), Sardinia, Italy, 2018. To gain access to the dataset, contact us at irena.orsolic@fer.hr.

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. Part of the dataset is analysed and described in  "The impact of user, system, and context factors on gaming QoE: a case study involving MMORPGs." Proceedings of Annual Workshop on Network and Systems Support for Games. IEEE Press, 2013. To gain access to the data set, contact us at mirko.suznjevic@fer.hr.

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 “Game Categorization for Deriving QoE-Driven Video Encoding Configuration Strategies for Cloud Gaming” submitted for review in the Special Issue on Delay-Sensitive Video Computing in the Cloud in ACM Transactions on Multimedia Computing, Communications. To gain access to the data set, contact us at ivan.slivar@fer.hr.

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  "Game Categorization for Deriving QoE-Driven Video Encoding Configuration Strategies for Cloud Gaming" submitted for review in the Special Issue on Delay-Sensitive Video Computing in the Cloud in ACM Transactions on Multimedia Computing, Communications. To gain access to the data set, contact us at ivan.slivar@fer.hr.