aCroSS: AI-driven cross-layer adaptive streaming for short video applications

Abstract

As short video applications gain popularity, researchers are exploring ways to enhance Quality of Experience (QoE) for short videos while maximizing network bandwidth efficiency. Despite the growing interest, existing Adaptive Bitrate (ABR) algorithms primarily concentrate on content prefetching strategies and often overlook the dynamic interaction between network congestion control and ABR. This interaction is especially critical for short video streaming, where network conditions can fluctuate rapidly, and user expectations for seamless playback are high. To address these challenges, we propose aCroSS, an AI-driven framework for adaptive short video streaming that jointly optimizes both the application and transport layers to enhance QoE and bandwidth utilization. The aCroSS algorithm leverages advanced machine learning techniques to adapt in real time to fluctuating network conditions and dynamic user behaviors, delivering a more robust and responsive streaming experience. Our simulation results demonstrate that aCroSS consistently outperforms existing baseline algorithms, achieving more than a 10% improvement in utility scores across various network trace datasets. This highlights the effectiveness of aCroSS in delivering superior performance in diverse streaming environments.