QoE Prediction for Gaming Video Streaming in O-RAN Using Convolutional Neural Networks

Abstract

The growing popularity of online and cloud gaming applications is reshaping the landscape of the entertainment industry and acting as a key driver of market growth. However, the dependency of these applications on network resources poses significant challenges to the communication infrastructure. This is particularly critical as network performance plays a key role in influencing user satisfaction during gameplay. Inevitably, these inherently interactive applications are also closely linked to the concept of quality of experience (QoE), which expresses the perceived quality of a service by end-users. In this paper, we leverage deep learning methodologies to develop an objective QoE prediction model. Specifically, the proposed prediction model investigates the effect of wireless network operation on the QoE of gaming video streaming. Employing a tailored multi-headed convolutional neural network (multi-headed CNN), the model can predict in real-time the transmission-related QoE value using measurable quality of service (QoS) parameters. To validate the effectiveness of the model, tests and evaluations were conducted in an open radio access network testbed environment equipped with O-RAN-compatible interfaces.

Keywords

• deep neural network
• gaming video
• open radio access network (Open RAN)
• quality of experience (QoE)
• QoE prediction