QoE-driven Link Quality Prediction for Video Streaming in Mobile Networks
Yitu Wang, Riichi Kudo, Yuya Aoki, Yoshifumi Morihiro, Kahoko Takahashi, Hisashi Nagata · 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) · 2022
The link quality prediction facilitates high quality video streaming over mobile networks. However, the existing link quality prediction algorithms focus on minimizing the gap between the ground truth and the prediction result, while it remains a challenge to exploit such information to achieve high quality video streaming with minimum Quality of Experience (QoE) degradation. The accurate link quality prediction is one of keys to enable beyond 5G/6G world. In this paper, we produce artificial intelligence (AI) based link quality prediction which consists two steps: 1. We explore and exploit the temporal correlation in time series to adaptively learn and predict its short-term behavior based on Gaussian Process (GP). 2. The GP-based prediction is tailored to maximize QoE by finding a proper piece-wise convex envelope of the predicted link quality in an online manner. By using the measured uplink throughputs, the video streaming QoE of the proposed framework were evaluated.