Immersive mixed reality in industrial applications requires low latencies; however, systems are commonly limited by the large amounts of generated data and the subsequent high traffic loads on the 5G network. Semantic or goal-oriented compression has emerged as a solution to this problem. In this study, we combine the generated data from several cameras at the network edge through multilevel semantic coding, which we refer to as MLcXR+. We define the overall MLcXR+ approach, including computation load placements on eas, and demonstrate feasibility in a 5G system with an open-source prototype on commodity hardware. We develop and evaluate two MLcXR+ modes: independent MLcXR+ (iMLcXR+), where the EASs operate independently, and dependent MLcXR+ (dMLcXR+), where the EASs form a chain of successive compression levels. From our evaluations with a reproducible dataset and our open-source prototype, we find that compared to prior state-of-the-art, our MLcXR+ approach can achieve a 3.5 times higher compression ratio factor, substantially reducing the data amounts sent over the 5G Data Network (DN) and processed at the Head-Mounted Display (HMD). We also find that iMLcXR+ at a single EAS, or dMLcXR+ with one or an appropriately configured higher number of EASs achieve the highest compression. Load balancing of the MLcXR+ computations over multiple EASs substantially reduces the iMLcXR+ compression performance, and can slightly increase the network transport delays of dMLcXR+ due to the compression chain. However, even load-balanced dMLcXR+ still incurs slightly less total computation (compression) and network transport delays than prior approaches, mainly due to the reduced HMD processing.