Emerging delay-sensitive interactive services, such as extended reality and remote operation, demand advanced congestion control algorithms (CCAs) capable of maintaining high data rates while keeping latency low in mobile networks. Existing CCAs typically rely on congestion signals such as packet loss, delay, or explicit congestion notification (ECN) marks, which can be delayed, imprecise, or require extensive upgrades to the network infrastructure. To address these limitations, physical resource block (PRB) utilization has recently been proposed as a reliable early congestion signal in mobile networks [1]. This paper introduces PRISM, a reinforcement-learning-based CCA that uses peak PRB utilization to regulate the send rate under challenging mobility and fading conditions. NS3 simulation results show that, in a mobility scenario where two PRISM flows coexist with two TCP Cubic flows and one TCP BBR flow, PRISM maintains a per-packet mean one-way delay (OWD) of 6.3ms and a $p_{99}$ OWD of 44.6ms, with negligible (0.02%) packet loss, over a 20min high-contention interval. However, over the same interval, the coexisting TCP flows experience 60x higher mean OWDs of 371-390ms and $p_{99}$ OWDs of 563-578ms, with 0.1% packet loss. PRISM achieves a 31x improvement in goodput-over-latency performance, indicating a favorable tradeoff for delay-sensitive applications.

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