桂林电子科技大学学报2026，Vol.46Issue(4)：363-371,9.DOI:10.16725/j.1673-808X.2023194
基于视频内容特征的自适应比特率算法
Adaptive bitrate algorithm based on video content features
摘要
Abstract
Adaptive bitrate(ABR)streaming dynamically adjusts video bitrates according to the network conditions to improve the users'quality of experience(QoE).However,existing ABR algorithms do not adequately consider the relationship between video content features and perceptual video quality,which limits their performance.To solve this problem,a video-content-aware adaptive bitrate algorithm is proposed.Subjective video quality assessment is employed to quantify the impact of different content features on perceptual quality scores.A deep reinforcement learning(DRL)model is then used to capture the relationship between spatiotempo-ral video-content features and perceptual video variations.In addition,distributed parallel training is adopted to accelerate model convergence,while principal component analysis(PCA)is applied to reduce feature dimensionality and improve training efficiency.Experimental results demonstrate that the proposed algorithm consistently improves QoE by approximately 3%～5%under both sta-ble and fluctuating network conditions with varying video chunk sizes.关键词
视频内容特征/自适应比特率算法/深度强化学习/用户体验质量/多代理Key words
video content features/adaptive bitrate algorithm/deep reinforcement learning/user experience quality/multi-agent分类
信息技术与安全科学引用本文复制引用
黄桂琳,王勇,刘世嘉..基于视频内容特征的自适应比特率算法[J].桂林电子科技大学学报,2026,46(4):363-371,9.基金项目
国家自然科学基金(61831013) （61831013）
广西创新驱动发展专项(桂科AA18118031) （桂科AA18118031）
桂林电子科技大学研究生教育创新计划(2023YCXB06) （2023YCXB06）
