  High throughput satellites (HTSs) outpace traditional satellites due to their
multi-beam transmission. The rise of low Earth orbit mega constellations
amplifies HTS data rate demands to terabits/second with acceptable latency.
This surge in data rate necessitates multiple modems, often exceeding single
device capabilities. Consequently, satellites employ several processors,
forming a complex packet-switch network. This can lead to potential internal
congestion and challenges in adhering to strict quality of service (QoS)
constraints. While significant research exists on constellation-level routing,
a literature gap remains on the internal routing within a single HTS. The
intricacy of this internal network architecture presents a significant
challenge to achieve high data rates.
  This paper introduces an online optimal flow allocation and scheduling method
for HTSs. The problem is presented as a multi-commodity flow instance with
different priority data streams. An initial full time horizon model is proposed
as a benchmark. We apply a model predictive control (MPC) approach to enable
adaptive routing based on current information and the forecast within the
prediction time horizon while allowing for deviation of the latter.
Importantly, MPC is inherently suited to handle uncertainty in incoming flows.
Our approach minimizes the packet loss by optimally and adaptively managing the
priority queue schedulers and flow exchanges between satellite processing
modules. Central to our method is a routing model focusing on optimal priority
scheduling to enhance data rates and maintain QoS. The model's stages are
critically evaluated, and results are compared to traditional methods via
numerical simulations. Through simulations, our method demonstrates performance
nearly on par with the hindsight optimum, showcasing its efficiency and
adaptability in addressing satellite communication challenges.
