Adaptive Video Bitrate Allocation for Remotely Operated Vehicles (ROV)
Creators: name: Afi, Eman Sarah
name: Triqui, Ons
name: Sayahi, Sofiane
name: Besbes, Hichem
name: Tlili, Fethi
Publisher: name: American University of Bahrain
Year: 2025
Subjects: classificationCode: 400203
subject: Automotive mechatronics and autonomous systems
classificationCode: 460304
subject: Computer vision

description: Establishing a stable and efficient connection between operators and remotely operated vehicles (ROVs) is essential for successful missions in challenging environments. Real-time video transmission is particularly critical, providing operators with visual feedback to navigate and control the vehicle effectively. This study aims to improve video transmission quality in a multi-camera ROV equipped with six cameras and various sensors. Our focus is to enhance the Quality-Aware Dynamic Rate Allocation (QADRA) system, which optimizes video quality by dynamically adjusting codec parameters like resolution and quantization parameters (QPs) based on expected peak signal-to-noise ratio (XPSNR) predictions. This enhancement addresses the unique challenges of achieving balanced video quality across multiple video streams.<br><br>The method involves developing a system that dynamically adapts codec parameters for each camera based on predefined bitrates, influenced by an assigned weight to each camera to control bitrate distribution and resulting video quality. By prioritizing certain video streams, this weighted system aims to optimize visual representation and thus improve the operator’s situational awareness.<br><br>Results from theoretical exploration and analysis indicate that this approach can enhance the internal distribution of video quality across multiple camera feeds, contributing to more effective decision-making during teleoperated vehicle operations. In conclusion, this research advances the multi-camera video transmission capabilities for teleoperated vehicles, addressing limitations in video quality management and improving operational effectiveness in remote vehicle control.Establishing a stable and efficient connection between operators and remotely operated vehicles (ROVs) is essential for successful missions in challenging environments. Real-time video transmission is particularly critical, providing operators with visual feedback to navigate and control the vehicle effectively. This study aims to improve video transmission quality in a multi-camera ROV equipped with six cameras and various sensors. Our focus is to enhance the Quality-Aware Dynamic Rate Allocation (QADRA) system, which optimizes video quality by dynamically adjusting codec parameters like resolution and quantization parameters (QPs) based on expected peak signal-to-noise ratio (XPSNR) predictions. This enhancement addresses the unique challenges of achieving balanced video quality across multiple video streams. The method involves developing a system that dynamically adapts codec parameters for each camera based on predefined bitrates, influenced by an assigned weight to each camera to control bitrate distribution and resulting video quality. By prioritizing certain video streams, this weighted system aims to optimize visual representation and thus improve the operator’s situational awareness. Results from theoretical exploration and analysis indicate that this approach can enhance the internal distribution of video quality across multiple camera feeds, contributing to more effective decision-making during teleoperated vehicle operations. In conclusion, this research advances the multi-camera video transmission capabilities for teleoperated vehicles, addressing limitations in video quality management and improving operational effectiveness in remote vehicle control.<br>