Practical Methods for Efficient Resource Utilization in Augmented Reality Services

This work presents a novel approach that adopts content caching techniques towards reducing computation and communication costs of Augmented Reality (AR) services. The application scenario under investigation assumes an environment of static objects, each one associated to a holographic content. The goal is to devise practical low-overhead methods so as to reduce the amount of resources above that are needed for the most resource-demanding AR process, namely object recognition. The proposed method is based on caching images using a combination of metrics to rank them such as: (i) an object popularity index which favours objects that are most probable to be requested for recognition, (ii) the percentage of times when the object label has been encountered in the past, (iii) the probability that an image is similar enough with already encountered past images with the same label. The aforementioned image caching method drastically reduces database searches and returns the matched object that satisfies the needs of object recognition. We also devise a binary decision operator that initiates the object recognition process only upon comparison of spatial data of the AR device with the targeted object. The resulting performance is measured using a client-server architecture and components such as Wireshark, Unity Profiler, and Python. For our proposed architecture we deploy an edge server to satisfy the demands of the AR service. Results indicate that the proposed methods can significantly reduce both the computational resources and the induced network traffic, thus improving user experience.