LooM: Learning-Based Multipath Scheduling for Out-of-Order Mitigation in Mobile Networks

Multipath transmission offers bandwidth aggregation capabilities for mobile networks. However, path heterogeneity and user mobility often lead to increased packet out-oforder (OFO) rate, causing buffer blocking, reduced throughput, and degraded transmission quality. To mitigate the OFO effect in multipath transmission, this paper proposes LooM, a learning-based multipath scheduler. LooM is designed to optimize throughput and OFO rate, employing a learning-based scheduling strategy to achieve the optimal packet scheduling under fluctuating paths. Particularly, LooM employs singleround scheduling as its basic unit, calculating the number of OFO packets across scheduling units to dynamically set path blocking delay, thereby adjusting packet transmission order to ensure in-order delivery. Simulation results show that, compared to traditional scheduling algorithms, LooM reduces the OFO rate by 16% while maintaining high throughput and achieving a lower packet loss rate.