Evaluating MPQUIC schedulers in dynamic wireless networks with 2D and 3D mobility

This study evaluates the performance of Multipath QUIC (MPQUIC) schedulers, which allow mobile devices to use multiple wireless networks for better throughput and reliability. Previous evaluations of MPQUIC schedulers are mostly limited to simple, two-dimensional (2D) scenarios, which do not capture the complexities of three-dimensional (3D) environments involving mobile or aerial devices. To address this, we implemented three 3D mobility models—Random Waypoint 3D, Reference Point Group Mobility 3D, and Gauss–Markov 3D—adapted from existing 2D models. We then assessed three non-learning MPQUIC schedulers (i.e., minRTT, BLEST, and ECF) and two learning-based schedulers (i.e., Peekaboo and Q-ReLeS) under varied conditions. Our results indicate that movement patterns, particularly random mobility, significantly affect scheduler performance. In stable network conditions, learning-based schedulers like Q-ReLeS outperform non-learning ones in download time and packet loss, but as conditions worsen, their advantages decrease, suggesting a need for further optimization in dynamic environments.