An increase in the use of smart and portable devices like smartphones, laptops, and tablets has led to a rise in the number of network interfaces and thus the number of possible channels for communication. However, the current approach over the Internet only employs a single path for a communication session. As an innovative and promising method for real-time transmission based on GRE-in-UDP encapsulation, which provides an IPv4 or IPv6 tunneling mechanism, this paper presents multipath throughput testing for the MPT network layer multipath communication library. We investigated the effectiveness of MPT's channel capacity aggregation while dealing with wired channels and examined scenarios in symmetric and asymmetric paths. Our network throughput measurements showed that MPT can efficiently aggregate the capacities of both symmetric and asymmetric paths. In this paper, we established a network topology that included a server, which we used for generating various quality of service (QoS) metrics. We measured how latency, transmission speed, packet loss rate, jitter, and the setting of the path weights influence throughput aggregation capability of the MPT communication library.

The demand for real-time 4K video streaming has introduced technical challenges due to the high bandwidth, low latency, and minimal jitter required for high-quality user experience. Traditional single-path networks often fail to meet these requirements, especially under network congestion and packet loss conditions, which degrade video quality and disrupt streaming stability. This study evaluates Multipath tunnel- Generic Routing Encapsulation (MPT-GRE), a technology designed to address these challenges by enabling simultaneous data transmission across multiple network paths. By aggregating bandwidth and adapting dynamically to network conditions, MPT-GRE enhances resilience, maintains quality during network disruptions, and offers throughput nearly equal to the sum of its physical paths’ throughput. This feature ensures that even if one path fails, the technology seamlessly continues streaming through the remaining path, significantly reducing interruptions. We measured key video quality metrics to assess MPT-GRE’s performance: Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Our results confirm that the MPT-GRE tunnel effectively improves SSIM, PSNR, and reduces MSE compared to single-path streaming, offering a more stable, high-quality viewing experience. Our results indicate that analyzing the SSIM, MSE, and PSNR values for 4K video streaming using the MPT tunnel demonstrates a significant performance improvement compared to a single path. The improvement percentages of the SSIM and PSNR values for the MPT tunnel are (29.05% and 29.04%) higher than that of the single path, while MSE is reduced by 81.17% compared to the single path.

Network packets may arrive out of their original order due to network delays, transmission speed variations, congestion, or uneven resource distribution. These factors cause significant challenges to network performance. These challenges result in jitter, packet loss, and reduced throughput, negatively affecting the efficient arrangement of packets. The Multipath tunnel-Generic Routing Encapsulation (MPT-GRE) architecture addresses this issue through a packet reordering mechanism designed for multipath GRE with User Datagram Protocol (UDP) encapsulation networks. This study investigates and analyses the path-specific delays, jitter, and transmission speed constraints to evaluate the influence of out-of-order packets on the MPT-GRE tunnel throughput aggregation capability. By comparing scenarios with and without the re-ordering mechanism, the results demonstrate that the reordering mechanism substantially improves the traffic throughput in symmetric and asymmetric channel configurations. Additionally, the study emphasizes the critical role of optimizing the reordering window parameter for effective performance. These findings confirm that packet reordering mechanisms significantly enhance MPT-GRE network performance by reducing the negative effects of delays and out-of-order arrivals.

Background/Objectives: Reliable skin-lesion AI requires evaluation beyond the development distribution. This study assessed whether a source-developed multi-class system preserves discrimination and reliability when transferred to an unseen smartphone-acquired domain. Methods: A source-only multimodal four-class classifier was developed using HAM10000 and Derm7pt and evaluated on PAD-UFES-20 without target-domain adaptation. Reliability was assessed using class-specific calibration, uncertainty-based error detection, class-conditional conformal prediction, selective classification, demographic subgroup auditing, controlled ablation, and an independent DDI out-of-distribution (OOD) and explainability (XAI) audit. Results: Across five prespecified seeds, macro-F1 decreased from 74.27 ± 1.36% on the source domain to 54.92 ± 2.60% on PAD-UFES-20. Calibration, conformal efficiency, and selective coverage also deteriorated externally, although macro-AUROC remained comparatively preserved. The independent DDI audit showed limited OOD separation, while explanation stability was interpreted only as descriptive evidence. Ablation analysis indicated that clinical metadata contributed importantly to external performance. Conclusions: Internally reproducible discrimination did not guarantee reliable external behavior. These findings support explicit external reliability auditing and specialist-supervised selective decision support before cross-device deployment.