QUASAR: Quad-based Adaptive Streaming And Rendering

As AR/VR systems evolve to demand increasingly powerful GPUs, physically separating compute from display hardware emerges as a natural approach to enable a lightweight, comfortable form factor. Unfortunately, splitting the system into a client-server architecture leads to challenges in transporting graphical data. Simply streaming rendered images over a network suffers in terms of latency and reliability, especially given variable bandwidth. Although image-based reprojection techniques can help, they often do not support full motion parallax or disocclusion events. Instead, scene geometry can be streamed to the client, allowing local rendering of novel views. Traditionally, this has required a prohibitively large amount of interconnect bandwidth, excluding the use of practical networks. This paper presents a new quad-based geometry streaming approach that is designed with compression and the ability to adjust Quality-of-Experience (QoE) in response to target network bandwidths. Our approach advances previous work by introducing a more compact data structure and a temporal compression technique that reduces data transfer overhead by up to 15×, reducing bandwidth usage to as low as 100 Mbps. We optimized our design for hardware video codec compatibility and support an adaptive data streaming strategy that prioritizes transmitting only the most relevant geometry updates. Our approach achieves image quality comparable to, and in many cases exceeds, state-of-the-art techniques while requiring only a fraction of the bandwidth, enabling real-time geometry streaming on commodity headsets over WiFi.