AuthorsS. Zadtootaghaj, S. Schmidt, and S. S. Sabet
EditorsC. Griwodz, and S. Moller
TitleQuality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions
AfilliationMachine Learning
Project(s)Department of Holistic Systems
Publication TypeProceedings, refereed
Year of Publication2020
Conference NameACM Multimedia Systems Conference 2020 (MMSys 2020)
PublisherAssociation for Computing Machinery (ACM)
Place PublishedNew York, NY, USA

The gaming industry is one of the largest digital markets for decades and is steady developing as evident by new emerging gaming services such as gaming video streaming, online gaming, and cloud gaming. While the market is rapidly growing, the quality of these services depends strongly on network characteristics as well as resource management. With the advancement of encoding technologies such as hardware accelerated engines, fast encoding is possible for delay sensitive applications such as cloud gaming. Therefore, already existing video quality models do not offer a good performance for cloud gaming applications. Thus, in this paper, we provide a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard. In addition, we investigate the performance of signal-based and parametric video quality models on the new gaming video dataset. Finally, we build two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation. Both models are based on perceptual video quality dimensions and can be used to optimize the resource allocation of gaming video streaming services.

Citation Key27250