Recent advances in mobile System-on-Chip (SoC) architectures–equipped with specialized AI accelerators such as GPUs, DSPs, and NPUs–have made on-device inference a viable alternative to edge-assisted processing. Mobile Augmented Reality (MAR) applications increasingly rely on multiple deep learning models to enable real-time interaction with virtual objects. Although several works aim to enhance MAR performance, they often overlook the runtime contention between AI inference and AR rendering tasks, which degrades both object quality and AI latency. In this paper, we introduce HBO+, a runtime framework for MAR applications that jointly controls virtual object triangle count and AI task placement on local and edge resources. By combining Bayesian optimization with heuristic algorithms, HBO+dynamically balances AI task performance and virtual object quality to improve overall system performance. We implement HBO+on Android and evaluate it using commercial smartphones and edge servers. The experimental results show that our method reduces the average latency of the AI task by up to 1.38× and improves the average virtual object quality by up to 30% compared to state-of-the-art baselines.

Edge computing has been developed as a low-latency data driven computation paradigm close to the end user to maximize profit, and/or minimize energy consumption. Edge computing allows each user’s task to analyze locally-acquired sensor data at the edge to reduce the resource congestion and improve the efficiency of data processing. To reduce application latency and data transferred to edge servers it is essential to consider data sharing for some user tasks that operate on the same data items. In this article, we formulate the data sharing-aware allocation problem which has as objectives the maximization of profit and minimization of network traffic by considering data-sharing characteristics of tasks on servers. Because the problem is${\sf NP-hard}$, we design the${\sf DSTA}$algorithm to find a feasible solution in polynomial time. We investigate the approximation guarantees of${\sf DSTA}$by determining the approximation ratios with respect to the total profit and the amount of total data traffic in the edge network. We also design a variant of${\sf DSTA}$, called${\sf DSTAR}$that uses a smart rearrangement of tasks to allocate some of the unallocated tasks for increased total profit. We perform extensive experiments to investigate the performance of${\sf DSTA}$and${\sf DSTAR}$, and compare them with a representative greedy baseline that only maximizes profit. Our experimental analysis shows that, compared to the baseline,${\sf DSTA}$reduces the total data traffic in the edge network by up to 20% across 45 case study instances at a small profit loss. In addition,${\sf DSTAR}$increases the total profit by up to 27% and the number of allocated tasks by 25% compared to${\sf DSTA}$, all while limiting the increase of total data traffic in the network.

The emergence of modern mobile System on Chips (SoCs), featuring robust neural network accelerators such as GPUs, DSPs, and NPUs, has made on-device inference a compelling alternative to cloud-assisted inference. Typical mobile augmented reality (MAR) applications enable users to interact with virtual objects, leveraging diverse Artificial Intelligence (AI) capabilities facilitated by a range of deep learning models. Several studies seek to improve the performance of MAR apps. However, they often overlook the computational concurrency between the AR tasks necessary to render virtual objects and the AI tasks, which can influence virtual object quality and AI inference response time. In this paper, we present HBO, a framework for MAR apps that trades off between AR and AI task performance. HBO leverages Bayesian optimization and heuristic algorithms to jointly manipulate the virtual objects' triangle count and AI task allocation for optimized MAR app performance. We have implemented HBO on Android and tested it on real smartphones and with real users. Our results show that HBO helps reduce the average AI task latency by up to 3.5x and increase the average virtual object quality by up to 38.7% compared to several stateof-the-art baselines.

Mobile Augmented Reality (MAR) apps may cause short battery life due to high-quality virtual objects rendered in the augmented environment. State-of-the-art solutions propose to balance energy consumption and user-experience using a static set of decimated object versions within the app. However, they do not consider that each object has unique characteristics, which highly influence how the user-perceived quality changes according to user-object distance and triangle count. As a result, they may lead to limited energy savings, a high storage overhead, and a high burden on the MAR app developer. In this paper, we propose eAR, an edge-assisted autonomous and energy-efficient framework for MAR apps designed to solve the limitations of state-of-the-art solutions. eAR features an offline software running on an edge server that leverages Image Quality Assessment (IQA) to model user-perceived quality for each virtual object in terms of triangle count and user-object distance. In addition, eAR features a runtime lightweight optimization algorithm that dynamically decides the most energy-efficient virtual object triangle count to request from the edge server based on (i) the per-object models of user-perceived quality, (ii) energy consumption models for mobile GPU and network interface, and (iii) a user path prediction system that estimates near-future user-object distances. eAR is completely autonomous and can be easily integrated into most MAR apps as an open-source library. Our results show that eAR can help reduce energy consumption by up to 16.5% while reducing storage overhead by almost 60% compared to existing schemes, with minimal MAR app developer effort and minimal impact on user-perceived quality.