AAV Video Encoding: Online Rate-Distortion Modeling and Multi-Timescale Rate Control

TY - JOUR

T1 - AAV Video Encoding

T2 - Online Rate-Distortion Modeling and Multi-Timescale Rate Control

AU - Yu, Zongyang

AU - Cao, Xianbin

AU - Yang, Peng

AU - Zheng, Dezhi

AU - Zhang, Haijun

AU - Quek, Tony Q.S.

AU - Wu, Dapeng Oliver

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2026

Y1 - 2026

N2 - This paper is concerned with rate control (RC) for autonomous aerial vehicle (AAV) video encodingto produce a stable video bitrate and high quality videos under specific constraints. To this aim, we theoretically investigate the relationships between encoding parameters and bitrate and distortion, and design an accurate online rate-distortion (R-D) model by employing linear regression and predictive artificial intelligence (AI) methods. Considering the sensitivity of AAV video encoding to latency and power consumption, we further establish the mathematical relationships between encoding parameters and encoding time and power consumption. By integrating the R-D and encoding time and power consumption models, we formulate a multi-timescale optimization problem and propose a novel algorithm to solve it. Specifically, we decompose it into a family of single-timescale problems via an alternating direction method of multipliers (ADMM). In addition, we design an iterative optimization scheme to solve the single-timescale problems with low computational complexity. Extensive experiments are conducted to validate the designed model and algorithm. Experimental results indicate that the designed model has small estimation errors, and the variance of Y-PSNR achieved by the proposed algorithm is not greater than 26.3% of its counterpart.

AB - This paper is concerned with rate control (RC) for autonomous aerial vehicle (AAV) video encodingto produce a stable video bitrate and high quality videos under specific constraints. To this aim, we theoretically investigate the relationships between encoding parameters and bitrate and distortion, and design an accurate online rate-distortion (R-D) model by employing linear regression and predictive artificial intelligence (AI) methods. Considering the sensitivity of AAV video encoding to latency and power consumption, we further establish the mathematical relationships between encoding parameters and encoding time and power consumption. By integrating the R-D and encoding time and power consumption models, we formulate a multi-timescale optimization problem and propose a novel algorithm to solve it. Specifically, we decompose it into a family of single-timescale problems via an alternating direction method of multipliers (ADMM). In addition, we design an iterative optimization scheme to solve the single-timescale problems with low computational complexity. Extensive experiments are conducted to validate the designed model and algorithm. Experimental results indicate that the designed model has small estimation errors, and the variance of Y-PSNR achieved by the proposed algorithm is not greater than 26.3% of its counterpart.

KW - AAV video encoding

KW - predictive artificial intelligence

KW - rate control

KW - rate-distortion model

UR - https://www.scopus.com/pages/publications/105013889127

U2 - 10.1109/TNSE.2025.3600297

DO - 10.1109/TNSE.2025.3600297

M3 - Article

AN - SCOPUS:105013889127

SN - 2327-4697

VL - 13

SP - 1723

EP - 1737

JO - IEEE Transactions on Network Science and Engineering

JF - IEEE Transactions on Network Science and Engineering

ER -