Fan Yi will present his FPO "Cross-Layer Telemetry and Optimization for Real-Time Interactive Applications in 5G Networks" on Tuesday, July 8, 2025 at 3pm in Friend 007. The members of his committee are as follows: Examiners: Kyle Jamieson (adviser), Ravi Netravali, Maria Apostolaki (ECE) Readers: Wyatt Lloyd, Yaxiong Xie (University at Buffalo, SUNY) All are welcome to attend. Abstract: Real-time applications such as interactive video conferencing now underpin daily communication, yet their performance over 5G networks is often inconsistent and unreliable. While 5G promises unprecedented speed, its internal complexity introduces significant fluctuations in latency and throughput that directly degrade the user's Quality of Experience (QoE). These performance issues persist because the network stack operates in functional silos, obscuring the complex, cross-layer interactions that are the true root cause of application-level degradation. The fundamental challenge lies in the highly dynamic wireless channel and the lack of cross-layer visibility. Dynamic channel conditions and millisecond-level scheduling at the physical and link layers continually reshape available capacity, yet application-layer congestion controllers perceive only averaged, coarse-grained feedback and therefore misinterpret the wireless artifacts as generic network congestion. This flawed perception causes them to overreact by unnecessarily throttling sending rates or increasing buffer delays, which impairs the user experience. This thesis tackles this challenge by building a suite of intelligent wireless systems that first expose millisecond-level cross-layer telemetry, and then exploit it for real-time 5G channel optimization and sensing. This work first introduces Athena, a novel measurement framework that correlates high-resolution telemetry from the 5G physical layer up to the video conferencing application. Building on this framework, Domino is developed as a first-of-its-kind diagnostic tool that automates the root-cause analysis of QoE degradation by modeling and searching a graph of causal relationships, successfully identifying 24 previously unknown event chains that impair 5G video conferencing. Moving from diagnosis to direct network enhancement, WaveFlex is the first smart surface for private CBRS 5G that monitors control channels and steers signals in real time, increasing median throughput by 19.5%. Finally, CAPER repurposes cellular measurements for privacy-preserving proximity sensing, achieving highly accurate proximity detection via a custom deep learning pipeline. Collectively, these contributions deliver a comprehensive paradigm for building intelligent wireless systems. By providing the essential tools for deep measurement (Athena), automated diagnostics (Domino), proactive network optimization (WaveFlex), and novel sensing capabilities (CAPER), this thesis establishes the foundational visibility and control necessary to unlock the full potential of 5G and future wireless networks.