Abstract
Mobile augmented reality (MAR) applications are still in a rather embryonic state but they are currently attracting significant attention from both academic and industry stemming from increased capabilities from the network side as well as the terminals. The ability of augmented reality services to overlay digital content over the physical space and surroundings result in an immersive metaverse type of environment which opens up a plethora of potential use cases and applications. As with respect to mobile wireless networks, mobility has been proved to be an important factor in service latency of MAR systems. Previous research mainly focused on various techniques of offloading processing without considering the decomposition of MAR functions and the effect of mobility in an explicit manner.To this end, our works in chapter 3 combine the mobile edge clouds (MEC) and MAR to propose an optimization framework with a rich set of MAR specific constraints. In chapter 4, we then consider the content aware aspect and allow the proactive caching of high probability 2D field of views (FoVs) of AR Objects (AROs) to be stored instead of caching the significantly larger and complex 3D original AROs. In chapter 5, multiple view streams could also be brought in as another solution to proactive caching. In this case, popular view streams are precached with preferred 3D AROs to improve the overall user experience during different mobility events. In chapter 6, MAR is incorporated with metaverse but such an extension seeks reliable and high quality support for the foreground interactions and background contents from these applications, which intensifies their consumption of energy, caching and computing resources. To tackle these challenges, a more flexible request assignment and resource allocation with more efficient processing are proposed through anchoring decomposed metaverse AR services at different edge nodes and proactively caching background metaverse region models embedded with target AROs.
As illustrated by a broad set of numerical investigations, the proposed set of solutions can efficiently decompose the MAR service to available edge cloud resources and hence increase decision making quality compared to other previously proposed and baseline schemes. Advanced terminals capabilities are also considered to further reduce service delay at an acceptable cost of energy consumption.
| Date of Award | 1 Aug 2023 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Vasilis Friderikos (Supervisor) & Osvaldo Simeone (Supervisor) |