MediaTek has unveiled a new artificial intelligence-driven network management technology designed to enhance cloud gaming performance. Named AI Active Queue Management (AI AQM), the system aims to optimize edge network traffic and significantly lower latency for players streaming titles via NVIDIA GeForce NOW, promising response times up to ten times faster.
The technology will be delivered via a firmware update to Customer Premises Equipment (CPE) operating on the MediaTek T930 platform. By embedding intelligence directly into the network hardware, AI AQM combines dynamic queue management with predictive algorithms. The system continuously evaluates key metrics, including wireless signal quality, local network traffic volume, and external congestion points. It then automatically adjusts packet handling policies to minimize both latency and jitter during active gaming sessions.
A primary advantage of MediaTek’s AI AQM is its seamless integration across existing infrastructures. The solution functions locally at the router level without requiring modifications to client applications, gaming platforms, or remote servers. Consequently, the optimization extends beyond cloud gaming to benefit other latency-sensitive applications, such as real-time video conferencing, remote desktop software, and general web browsing.
MediaTek emphasized that the optimization process respects user privacy. Instead of employing Deep Packet Inspection (DPI) to analyze payload content, the AI algorithms rely strictly on traffic metadata to identify delay-sensitive applications. This methodology enables effective bandwidth prioritization while maintaining user privacy standards.
According to HC Hwang, General Manager of Wireless Communication Systems and Partnerships at MediaTek, introducing AI-driven congestion control to the network edge establishes a foundation for next-generation home entertainment systems. By mitigating local network bottlenecks, the technology aims to narrow the performance gap between cloud-streamed gameplay and traditional local hardware rendering.
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