For real-time voice and video calling scenarios on mobile terminals, traditional Android communication modules often suffer from slow call setup, unstable audio routing, amplified media link jitter, and untimely session recovery under conditions of weak network, cross-network handover, and foreground/background switching. To achieve the ultimate goal of "fast call setup, stable maintenance, and smooth recovery," a layered high-reliability calling module for mobile VoIP was created. This module uses a five-layer structure: application orchestration layer, session service layer, transport adaptation layer, reliability enhancement layer, and observation and evaluation layer. It establishes unified control logic for aspects such as the Android Telecom/Jetpack calling interface, SIP and WebRTC signaling adaptation, link quality scoring, cross-path selection, limited redundancy forwarding, and call state machine recovery mechanisms. Based on the prototype system, 1200 call experiments were conducted in four scenarios: Wi-Fi, 4G, 5G, and 4G/5G handover. The results show that, compared to the single-path baseline solution, this module can increase the call setup success rate from 93.7% to 98.9%, reduce P95 call setup latency by 38.7%, reduce packet loss rate during handover by 56.3%, and improve the average subjective MO by 11.3%. Research indicates that while ensuring improved reliability through layered decoupling centered on calling, it significantly enhances the continuity and recoverability of real-time calls on Android devices.

Junhao Su, Design and Implementation of Android High Reliability Communication Module Based on Hierarchical Architecture. International Journal of Neural Network (2026), Vol. 5, Issue 1: 79-89. https://doi.org/10.38007/NN.2026.050109.

[1] Zheng, H. (2026, April). Research on Cloud-Edge Collaborative Elastic Computing and Cost Optimization for High-Concurrency Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1489-1496). IEEE.

[2] Liu, H. (2025, December). Mlops Model Deployment System for Multi-Cloud Environments and Improvement of Commercial AI Service Availability. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 1047-1052). IEEE.

[3] Hong, Y. (2025, December). Chain Demand Mutation Identification and Emergency Response Decision-Making Integrated With Social Media Sentiment Analysis. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 425-432). IEEE.

[4] Chen, M. (2026, March). Design of a Privacy-Oriented AI Compliance Hook System Based on Static Code Analysis. In 2026 IEEE Madhya Pradesh Section Conference (MPCON) (pp. 648-654). IEEE.

[5] Lyu, N. (2026, April). Toward Robust AI Agents: A Closed-Loop Task Planning–Execution–Feedback Framework for Open Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1182-1188). IEEE.

[6] Zhang, K. (2025, December). Research on Cross-Selling Precision Marketing Strategy Based On Xgboost Model and SHAP Explanatory Framework. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-7). IEEE.

[7] Zhu, P. (2025, December). Construction of Multi-Scale Biostatistical Analysis Framework and its Application in Biomedical Signal Feature Recognition and Classification. In 2025 5th International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE.

[8] Chang, C. W. (2026). Privacy Infrastructure Vulnerability Mining and Automated Framework Based on Multimodal AI. Procedia Computer Science, 279, 702-711.

[9] Wang, Y. (2026). Construction of Supply Chain Demand Forecasting Algorithm Based On Time Series Data Analysis and Improvement of Its Management Efficiency. Procedia Computer Science, 281, 484-493.

[10] Wang, Z. (2026). Mineral Commodity Time-Series Cycle Modeling and Feature Learning Algorithm Based On Improved Transformer. Procedia Computer Science, 281, 1347-1356.

[11] Chen, J. (2026). Construction of a Cloud-Native High-Performance Service Engineering System for Real-Time Decision-Making Platforms.

[12] Qian, X. (2026). Supply Chain Collaboration Mechanisms Driven by Demand Orchestration in Omni-Channel Retail Environments.

[13] Wang, Y. (2025). Application of Data Completion and Full Lifecycle Cost Optimization Integrating Artificial Intelligence in Supply Chain.

[14] Sun, J. (2025). Quantile Regression Study on the Impact of Investor Sentiment on Financial Credit from the Perspective of Behavioral Finance.

[15] Chen, M. (2025). Research on Automated Risk Detection Methods in Machine Learning Integrating Privacy Computing.

[16] Wu, Y. (2025). Optimization of Generative AI Intelligent Interaction System Based on Adversarial Attack Defense and Content Controllable Generation.

[17] Wu, Y. (2025). Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. Machine Learning Theory and Practice, 5(1), 64-75.

[18] Liang, Q. (2026). Research on the Renovation Design Path for Enhancing the Efficiency of Mixed-Use Office and Retail Spaces. European Journal of AI, Computing & Informatics, 2(2), 163-170.

[19] Liang, Q. (2026). Reconstruction of Commercial Building Space Reuse Mode Driven by Composite Business Types. International Journal of Engineering Advances, 3(1), 107-113.

[20] Gao, Y. (2026). The Role and Practice of Delaware Law in Global Cross-border M&A Transactions. International Journal of Law, Policy & Society, 2(1), 38-46.

[21] Gao, Y. (2026). Application of Delaware Corporate Law in Cross-Border M&A: Business Structures, Contractual Risk, and Case-Based Lessons. Economics and Management Innovation, 3(2), 128-136.

[22] Yin, J. (2025). Research on Financial Time Series Prediction Model Based on Multi Attention Mechanism and Emotional Feature Fusion. Socio-Economic Statistics Research (2025), 6(2), 161-169.

[23] Wang, N. (2026). Research on Evaluation and Optimization Models of Enterprise Resource Allocation Efficiency from a Data-driven Perspective. Advances in Computer and Communication, 7(1).

[24] Su, J. (2026). Research on Android Real-time Communication System Architecture and High-reliability Assurance Pathways Integrating AI-based Anomaly Detection Mechanisms. Engineering Advances, 6(2).

[25] Liang, Q. (2026). How Architectural Design and Utility Infrastructure Impacts AI Supporting Campus and Drive Future Innovation, Operational Efficiency and Sustainable Advancement in Utility-Critical Environment. European Journal of Engineering and Technologies, 2(2), 71-77.

[26] Wu, Y. (2025). Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. Machine Learning Theory and Practice, 5(1), 64-75.

[27] Liu, D., Shen, Q., & Liu, J. (2026). The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density. Computation, 14(1), 22.

[28] Chen, J. (2026). Elastic Scaling and Stability Assurance Mechanisms for Distributed Systems under High-Throughput Scenarios.

[29] Gao, Y. (2026). Research on the Design of Governance Structure for Private Equity Funds and the balance of GP and LP Rights.

[30] Huang, J. (2026). From Policy Authorization to Practical Execution: A Decision-Support Framework for Implementing Housing Supply Strategies in the United States. Strategic Management Insights, 3(1), 24-31.