QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning

Abstract

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in realtime, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

“…With advancements in wireless communication technology, sixth generation (6G) is bringing advanced technologies, such as mm-Wave [ 1 , 2 , 3 ], unmanned aerial vehicles (UAVs) [ 4 , 5 , 6 ], tera-hertz (THz) communications [ 7 , 8 , 9 ], intelligent reflecting surfaces (IRS) [ 10 , 11 , 12 ], non-orthogonal multiple access (NOMA) [ 13 , 14 , 15 ], etc., to overcome the limitations of prior wireless generations. Indoor positioning systems (IPSs) and location-based services have become a fundamental requirement for many Industrial Internet of Things (IIoT) applications [ 16 , 17 , 18 ].…”

AoI‐driven SWIPT‐enabled UAV trajectory optimization for two‐way relaying

“…where a 1 = P(1 − 𝜌)∕𝜎 2 . By considering the PS protocol, the received power at the UAV can be written as 28…”

“…In recent years, the popularity of unmanned aerial vehicles (UAVs) has grown due to their miniaturization, weight down to 19 g with 16 cm wingspan. 2 Moreover, UAVs offer distinctive advantages, including high mobility, cost-effectiveness, and deployment flexibility, which have propelled UAV-assisted wireless communication networks into a burgeoning research field embraced by both academia and industry. The utilization of UAVs as aerial mobile relays in cooperative communication systems has been widely discussed and investigated in the open literature.…”

Multi-UAV Energy Consumption Minimization using Deep Reinforcement Learning: An Age of Information Approach

“…[1][2][3][4] In particular, in environments where groundbased stations are unreliable, UAVs can act as aerial base stations to provide communications during disasters. [5][6][7][8][9] In a disaster environment that is dynamically changing and requires real-time status updates, it is important to maintain the freshness of the collected data for immediate response and action. Age of Information (AoI) [10] is a metric to measure the freshness of data, which considers the overall temporal aspect of data from its generation to its delivery to the end user.…”