Distributed virtual environments (DVEs) are challenging to create as the goals of consistency and responsiveness become contradictory under increasing latency. DVEs have been considered as both distributed transactional databases and force-reflection systems. Both are good approaches, but they do have drawbacks. Transactional systems do not support Level 3 (L3) collaboration: manipulating the same degree-of-freedom at the same time. Force-reflection requires a client-server architecture and stabilisation techniques. With Consensus Based Networking (CBN), we suggest DVEs be considered as a distributed data-fusion problem. Many simulations run in parallel and exchange their states, with remote states integrated with continous authority. Over time the exchanges average out local differences, performing a distribued-average of a consistent, shared state. CBN aims to build simulations that are highly responsive, but consistent enough for use cases such as the piano-movers problem. CBN's support for heterogeneous nodes can transparently couple different input methods, avoid the requirement of determinism, and provide more options for personal control over the shared experience. Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000's of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system.

Mobile HMDs must sacrifice compute performance to achieve ergonomic and power requirements for extended use. Consequently, applications must either reduce rendering and simulation complexity - along with the richness of the experience - or offload complexity to a server. Within the context of edge-computing, a popular way to do this is through render streaming. Render streaming has been demonstrated for desktops and consoles. It has also been explored for HMDs. However, the latency requirements of head tracking make this application much more challenging. While mobile GPUs are not yet as capable as their desktop counterparts, we note that they are becoming more powerful and efficient. With the hard requirements of VR, it is worth continuing to investigate what schemes could optimally balance load, latency and quality. We propose an alternative we call edge-physics: streaming at the scene-graph level from a simulation running on edge-resources, analogous to cluster rendering. Scene streaming is not only straightforward, but compute and bandwidth efficient. The most demanding loops run locally. Jobs that hit the power-wall of mobile CPUs are off-loaded, while improving GPUs are leveraged, maximising compute utilisation. In this paper we create a prototypical implementation and evaluate its potential in terms of fidelity, bandwidth and performance. We show that an effective system which maintains high consistencies on typical edge-links can be easily built, but that some traditional concepts are not applicable, and a better understanding of the perception of motion is required to evaluate such a system comprehensively.

Real-time interactive systems such as virtual environments have high performance requirements, and profiling is a key part of the optimisation process to meet them. Traditional techniques based on metadata and static analysis have difficulty following causality in asynchronous systems. In this paper we explore a new technique for such systems. Timestamped samples of the system state are recorded at instrumentation points at runtime. These are assembled into a graph, and edges between dependent samples recovered. This approach minimises the invasiveness of the instrumentation, while retaining high accuracy. We describe how our instrumentation can be implemented natively in common environments, how its output can be processed into a graph describing causality, and how heterogeneous data sources can be incorporated into this to maximise the scope of the profiling. Across three case studies, we demonstrate the efficacy of this approach, and how it supports a variety of metrics for comprehensively bench-marking distributed virtual environments.