A Platform-Agnostic Publish–Subscribe Architecture with Dynamic Optimization

Real-time media streaming over publish–subscribe platforms is increasingly vital in scenarios that demand the scalability of event-driven architectures while ensuring timely media delivery. This is especially true in multi-modal and resource-constrained environments, such as IoT, Physical Activity Recognition and Measure (PARM), and Internet of Video Things (IoVT), where integrating sensor data with media streams often leads to complex hybrid setups that compromise consistency and maintainability. Publish–subscribe (pub/sub) platforms like Kafka and MQTT offer scalability and decoupled communication but fall short in supporting real-time video streaming due to platform-dependent design, rigid optimization, and poor sub-second media handling. This paper presents FrameMQ, a layered, platform-agnostic architecture designed to overcome these limitations by decoupling application logic from platform-specific configurations and enabling dynamic real-time optimization. FrameMQ exposes tunable parameters such as compression and segmentation, allowing integration with external optimizers. Using Particle Swarm Optimization (PSO) as an exemplary optimizer, FrameMQ reduces total latency from over 2300 ms to below 400ms under stable conditions (over an 80% improvement) and maintains up to a 52% reduction under adverse network conditions. These results demonstrate FrameMQ’s ability to meet the demands of latency-sensitive applications, such as real-time streaming, IoT, and surveillance, while offering portability, extensibility, and platform independence without modifying the core application logic.