As OTT video consumption grows, residential Wi-Fi has become a critical part of the content delivery chain. Most in-home video traffic now relies on wireless connectivity, making Wi-Fi performance a direct factor in viewer Quality of Experience. Even when streaming platforms and CDNs are optimised, the final connection between the gateway and consumer device often remains unpredictable. Interference, congestion, and unstable wireless conditions can cause buffering, reduced video quality, and customer dissatisfaction.

The demonstration recreates a dense multi-dwelling unit (MDU) environment where overlapping Wi-Fi networks compete for spectrum during peak evening hours. Families stream video, children play online games, smart devices stay connected, and remote workers join online meetings. As congestion increases, some apartments experience unstable latency, poor roaming, and degraded video quality.
An AI-driven optimisation engine continuously analyses telemetry, RF conditions, device behaviour, and Quality of Experience indicators across the building. Using predictive analytics, the system identifies interference patterns and automatically applies optimisation actions such as channel reassignment, power adjustment, and intelligent client steering. Real-time dashboards and heat maps demonstrate how network performance improves dynamically as AI adapts to changing wireless conditions.