Research on Dynamic Optimization Strategy for Cross-platform Video Transmission Quality Based on Deep Learning

Abstract

This paper proposes a novel dynamic optimization strategy for cross-platform video transmission quality based on deep learning techniques. The strategy addresses two critical challenges in video streaming services: maintaining consistent quality across diverse platforms and optimizing resource utilization under varying network conditions. A comprehensive framework is developed, integrating multi-dimensional feature extraction, quality assessment, and adaptive optimization components. The framework implements an advanced deep learning architecture specifically designed for real-time quality prediction and adaptation. Experimental results demonstrate significant improvements in quality maintenance, with average quality scores increased by 27.3% while reducing bandwidth consumption by 31.5%. The system achieves a 42.8% reduction in adaptation latency compared to conventional approaches, while maintaining consistent quality levels across different platforms. Performance evaluation conducted on extensive datasets shows that the proposed method outperforms existing solutions in terms of quality stability and resource efficiency. The implementation demonstrates robust performance across diverse network conditions, with quality degradation contained within acceptable limits during adaptation periods. The system successfully maintains target buffer levels in 97.4% of test cases, with average rebuffering duration reduced by 65.8%. These results validate the effectiveness of the proposed strategy in optimizing video transmission quality while ensuring efficient resource utilization across different platforms.