We also measure several performance metrics associated with the quality of experience, namely, frame rate, packet loss rate, and image quality, with various configuration settings. Next, we measure VR traffic patterns, including frame size and data and packet rates with various settings, such as different resolution and encoding options.

First, we investigate the benefits of VR offloading in terms of computational load and power consumption reduction for a client device compared to standalone operation. In this work, we aim to provide an understanding of various characteristics of VR computation offloading through comprehensive experiments conducted using a prototype testbed for edge-assisted VR processing and streaming. Offloading heavy virtual reality (VR) computational operations to a network edge computation entity is receiving increasing attention as a tool to wirelessly and energy efficiently provide low-end client devices with high-quality and immersive interactive VR services anytime and anywhere across the globe. Through a conceptual demonstration, we also show that the presented rendering scheme can be effectively explored for augmenting real scene images, captured or perceived by augmented and mixed reality devices, with high quality ray-traced images. Our mobile distributed rendering system achieved a few frames per second when ray tracing 1024 × 1024 images, using only 16 low-end smartphones, for large 3D scenes, some with more than 10 million triangles. Then, to avoid performance impairment caused by the high latency and low bandwidth of mobile networks, we propose using a static load balancing strategy, which we found to be more appropriate for the vulnerable mobile clustering environment than a dynamic strategy. To cope with the limited processing power and memory space, we first present an effective 3D scene representation scheme suitable for mobile GPU rendering. To investigate the possibility of mobile cluster-based rendering of large datasets, we developed a mobile GPU ray tracer that renders nontrivial 3D scenes with many millions of triangles at an interactive frame rate on a small-scale mobile cluster. However, only a few attempts have been made to explore mobile computing clusters that can be easily built using commodity smartphones and tablets. This suggests that familiarity with the environment plays an important role, and with improved image quality and increased use, mixed reality applications may perform better than conventional 3D echocardiography viewing systems.Ĭluster computing has attracted much attention as an effective way of solving large-scale problems.

Participants familiar with mixed reality were quicker to orient in the tasks. The results showed that virtual objects in mixed reality provided easier spatial orientation and morphological understanding despite lower perceived image quality. The user study required eight participants to do timed tasks and rate their experience. In this work, we describe the design of a system for rendering 4D (3D + time) cardiac ultrasound data as virtual objects and evaluate it for ease of spatial orientation by comparing it with a standard clinical viewing platform in a user study. Mixed reality devices enhance depth perception, spatial awareness, interaction, and integration in the physical world, which can prove advantageous with 3D cardiac ultrasound images. Until recently, 3D cardiac ultrasound has been visualized on a flat screen by using volume rendering. Spatial orientation is an important skill in structural cardiac imaging.

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