Jin Heo

Jin Heo, Vic Wang, Ketan Bhardwaj et al.

In distributed multimedia applications, content is often delivered to users in a degraded form due to network-induced lossy compression. Real-time and interactive use cases like cloud gaming, which render content on the fly, require low latency and are hosted at resource-constrained edge servers. We present a new insight: when rendered content is delivered over a network with lossy compression, high-quality rendering can be ineffective in improving user-perceived quality, leading to a poor return on computing resources. Leveraging this observation, we built Stimpack, a novel system that adaptively optimizes game rendering quality by balancing server-side rendering costs against user-perceived quality. The system uses a mechanism that quantifies the efficiency of resource usage to maximize overall system utility in multi-user scenarios. Our open-sourced implementation and extensive evaluations show that Stimpack achieves up to 24% higher service quality and serves twice as many users with the same resources compared to baselines. A user study further validates that Stimpack provides a measurably better user experience.

Jin Heo, Gregorie Phillips, Per-Erik Brodin et al.

Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object detection and simultaneous localization and mapping are computationally intensive to mobile devices of limited resources and often offloaded on the edge. Offloading LiDAR perceptions requires compressing the raw sensor data, and lossy compression is used for efficiently reducing the data volume. Lossy compression degrades the quality of LiDAR point clouds, and the perception performance is decreased consequently. In this work, we present an interpolation algorithm improving the quality of a LiDAR point cloud to mitigate the perception performance loss due to lossy compression. The algorithm targets the range image (RI) representation of a point cloud and interpolates points at the RI based on depth gradients. Compared to existing image interpolation algorithms, our algorithm shows a better qualitative result when the point cloud is reconstructed from the interpolated RI. With the preliminary results, we also describe the next steps of the current work.