**4.1 Adaptive video streaming**

Kim et al. [3, 5] design a dynamic control algorithm for time-average streaming quality (i.e., peak-signal-to-noise ratio (PSNR)) maximization subject to transmit buffer stability in wireless video networks. Koo et al. [6, 7] also propose a novel dynamic adaptive streaming over HTTP (DASH)-based mechanism for video streaming quality maximization under the consideration of battery status, LTE data quota, and stability in hybrid LTE and WiFi networks.

### **4.2 Networks**

Neely et al. [8] proposed a novel dynamic multi-hop routing algorithm which is for energy-efficient data/packet forwarding in wireless ad hoc and sensor networks subject to queue stability.

#### **4.3 Security applications: surveillance monitoring**

Mo et al. [9] design a deep learning framework for CCTV-based distributed surveillance applications. In the system, multiple deep learning frameworks exist; and each deep learning model is with its own configurations. In this situation, there exists a tradeoff between complexity and performance. Therefore, the proposed CCTV-based surveillance algorithm adaptively selects a deep learning model depending on queue-backlog in the system for recognition performance maximization subject to CCTV queue stability. Kim et al. [10] also design a novel face identification deep learning frameworks for CCTV-based surveillance platforms. Instead of having multiple deep learning models, this system has one learning system (based on OpenFace open-source software library) and controls the sampling rates of the CCTV camera. Finally, the proposed decision-making algorithm dynamically selects CCTV sampling rates for recognition performance maximization subject to CCTV queue stability.
