ADR-X: ANN-Assisted Wireless Link Rate Adaptation for Compute-Constrained Embedded Gaming Devices

The wireless channel between gaming console and accessories e.g. controllers and headsets, experiences extremely rapid variations due to abrupt head and hand movements amidst an exciting game. In the absence of prior studies on wireless packet losses for console gaming, through extensive evaluations and user studies, we find that state-of-the-art rate adaptation schemes, unable to keep up with these rapid changes, experience packet loss rates of 2-10% while loss rates that are $10\times$ lower (0.1-0.5%) are required to ensure a high quality gaming experience. We present ADR-X, an ANN-based contextual multi-armed bandit rate adaptation technique that continuously predicts and tracks the channel and picks appropriate data rates. A key challenge for ADR-X is that it must run on power and compute constrained embedded devices under realtime constraints. ADR-X addresses this challenge by meticulously crafting an ANN that leverages existing communication theory results to incorporate domain knowledge. This allows ADR-X to achieve $10\times$ lower packet losses than existing schemes while also running $100\times$ faster than state-of-the-art reinforcement learning schemes, making it suitable for deployment on embedded gaming devices.