Cost-Aware Uplink MPQUIC Scheduling via Multi-Objective Bayesian Optimization

Multipath QUIC (MPQUIC) enables simultaneous uplink transmission over heterogeneous access networks such as Wi-Fi and LTE, improving reliability and performance. However, aggressive LTE utilization increases operational cost, creating an inherent trade-off between upload delay and cellular usage. Existing MPQUIC schedulers typically optimize a single performance objective and operate at fixed points within this trade-off space, without explicitly supporting cost-aware operation. This paper formulates uplink MPQUIC scheduling as a multi-objective optimization problem that jointly considers maximum upload completion time and total LTE usage. We propose a Bayesian Optimization-based framework that treats the MPQUIC system as a black box and systematically explores probabilistic path selection configurations to uncover Pareto-efficient operating points. Rather than committing to a predefined scheduling policy, the framework exposes a spectrum of delay--cost trade-offs without modifying protocol internals. Experiments conducted using the Mininet-WiFi emulator show that the proposed approach characterizes a wide delay--cost region and identifies configurations that achieve substantial LTE savings (up to 80%) with controlled increases in upload time. The results further indicate that, under higher contention levels, systematic multi-objective exploration provides increased flexibility compared to fixed-policy schedulers in cost-aware heterogeneous uplink deployments.