Abstract
Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.