QoE-Aware Cost-Minimizing Bandwidth Renting for Satellite-as-a-Service enabled Multiple-Beam SATCOM Systems

The advent of Satellite as a Service (SaaS) platforms
has empowered satellite service providers (SPs) to rent portions
of satellite capacity from infrastructure providers (IPs) to cater
to the diverse demands of their users across multiple satellite
services. To effectively manage costs and maintain a high Quality
of Experience (QoE) for numerous concurrent connections, SPs
should secure flexible capacity from IPs. However, the irregular
and unpredictable nature of traffic demands from various
applications complicates the capacity-renting framework.This
study presents a dynamic capacity allocation framework that
efficiently handles diverse traffic flows with varying arrival
rates, aiming to minimize rental costs while meeting blocking
probability and QoE requirements. Utilizing the 𝑀𝑡 /𝑀𝑡 /1 queuing
model and a continuous-time Markov chain, the technical
designs are framed as a statistical optimization problem. In
this context, the system waiting-queue lengths are estimated using
the transient probabilities of Kolmogorov equations. Subsequently,
cumulative distribution functions are employed to re-formulate
this challenging stochastic optimization problem into a convex
form, which can be tackled through the Lagrangian duality
method. Through extensive simulations and numerical assessments,
we illustrate our method’s efficacy, with the proposed algorithm
outperforming benchmarks by reducing costs by up to 9.85%
and 3.1%.