SCRAS Server-Based Crosslayer Rate-Adaptive Video Streaming over 4G-LTE for UAV-Based Surveillance Applications

Abstract

This research focuses on intelligent unmanned aerial vehicle (UAV)-based, real-time video surveillance to ensure better monitoring and security of remote locations over 4G-LTE cellular networks by maximizing end-user quality of experience (QoE). We propose a novel server-based crosslayer rate-adaptive scheme (SCRAS) for real-time video surveillance over 4G-LTE networks using UAVs. Our key contributions are: (1) In SCRAS, mobile UAVs having preprogrammed flight co-ordinates act as servers, streaming real-time video towards a remote client; (2) server-side video rate adaptation occurs in 4G-LTE based on the physical characteristics of the received signal conditions due to variations in the wireless channel and handovers; (3) SCRAS is fully automated and independent of client assistance for rate adaptation, as it is intended for real-time, mission-critical surveillance applications; (4) SCRAS ensures that during rate adaptation, the current video frame should not be damaged by completing the current group of packet (GoP) before adaptation. Our simulations in NS3 provide credible evidence that SCRAS outperforms recently proposed schemes in providing better QoE for real-time, rate-adaptive, video surveillance over 4G-LTE under varying channel quality and frequent handovers that occur during flight by UAVs.

IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

“…Often farmers will have to use one UAV type for monitoring of the field and another type of UAV for spraying purposes, as several factors come into play like UAV flight time, flying altitude, flight speed, camera, and other sensors' limitations and on-board hardware processing [64], [44], [65]. Jawad et al [65] have implemented a practical solar-powered wireless power transfer (WPT) based drone field-landing platform for drone battery recharging; authors claim 97% battery saving and flight endurance time increase from 25 minutes to 850 minutes on the specific X525 drone used in their study, when this system is augmented with a specialized sleep-wake strategy of WSN sensor nodes communicating with the drone.…”

FPV Video Adaptation for UAV Collision Avoidance

“…The research in [10] utilized a deep reinforcement learning (RL) agent to obtain the optimal bit-rate selection strategy under varying channel conditions with the aim of maximizing the general quality of experience, rather than for a specific application. Video adaptation for the specific application of UAV surveillance was studied in [11] and [12]. The work in [11] presented a cross-layer rate-adaptive algorithm meant for UAV surveillance applications.…”

“…Video adaptation for the specific application of UAV surveillance was studied in [11] and [12]. The work in [11] presented a cross-layer rate-adaptive algorithm meant for UAV surveillance applications. In [12], video adaptation was performed by transmitting a number of layered video streams from the UAV.…”

“…Applicable to FPV Video Adaptation Goal Obstacle Detection Technique [7] Minimize end to end video delay Not applicable (N/A) [9] N/A Sensors [10] Maximize quality of experience N/A [11] Real-time surveillance N/A [12] Two possible modes: maximize robustness or throughput N/A…”

“…where e(t) is the packet error rate in (11). We note that the transmission process of a frame ends after a maximum of n MRT (t) such transitions, or after reaching state s 0 .…”