## 3 Related Work
A hybrid hierarchical and multi-controller SDN architecture to tackle resource management and minimise SDNC signalling overhead in heterogeneous 3D networks is proposed [43]. The SDNCs, handling ground, aerial, and satellite layers, caters towards layer-specific QoS requirements and routing, while the E2E routes are combined from the intra-layer ones in the service composition layer. Service-Customised Network (SCN) for Immersive Media in SAGINs is studied in [44]. The concept proposes hierarchical SDN deployment for increased management capabilities and usage of edge servers for reduced end-to-end latency. It also introduces a DRL-enhanced multi-path Transport Control Protocol (TCP) routing strategy, which uses network state as an input and user Quality of Experience as feedback. Another hierarchical multi-tier SDN-based framework for integrated vehicular networks is proposed in [45], which introduces multi-level SDNCs (local, regional, national, and global domains) that communicate to enable coordination across segments and optimise network services. Summing up, SotA approaches strongly lean towards exploiting SDN for 3D network architectures. With increasing complexity, heterogeneity, and dynamics comes the need for efficient and tailored routing solutions. AI/ML-based methods, especially GNN-based ones, outperform conventional routing algorithms and enable good generalisation in complex environments. The latter is achieved thanks to the GNN ability to preserve the graph structure of data (compared to Deep Neural Network (DNN) architectures) and perform embedding operations. The node/edge feature embedding enables encoding of spatial and relational dependencies of the input data into low-dimensional space, taking into account not only the feature values but also the connectivity between nodes. In the context of DRL methods, GNNs bring multiple benefits that include learning meaningful state representations, improving output policy quality, reasoning, and model generalisation. As GNNs can process different-sized graphs, compared to traditional fixed-size DRL, they enable operation in frequently changing environments, such as NTNs and LEO, eliminating the need for multiple agents or complex architectures supporting state representation processing into a fixed-size input (as it would be in the DNN case). While deeply investigated in SotA, the usage of GNNs and DRL for routing, and their benefits regarding performance, QoS provisioning, or load balancing, as well as the SDN CP overhead, scalability aspects, and operator, profits are usually overlooked. Moreover, there is a general lack of methods for AI-driven 3D routing that consider the performance of individual network segments in addition to the E2E one. Although numerous distributed routing approaches are proposed (also GNN-based ones), these usually focus on selecting the next hop, giving limited abilities in terms of global-level network optimisation. This gap is filled by proposing an intelligent QoS-aware routing approach for SDN-based 3D networks that optimises load distribution, considers network operator priorities, and limits the impact on the SDN CP caused by frequent rerouting.