Adaptive Video Streaming With Automatic Quality-of-Experience Optimization

Video streaming has grown tremendously in recent years and it is now one of the main applications on the Internet. Due to the networks' inherent bandwidth fluctuations, various rate-adaptive streaming algorithms have been developed to compensate for such fluctuations to improve Quality-of-Experience (QoE). However, in practice, the preference for QoE typically differs significantly across different viewers and there is no systematic way so far to comprehensively incorporate different sets of conflicting QoE objectives into the algorithm design. Thus, it is not surprising that the QoE performance achieved by the existing algorithms is in fact far from optimal. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) that can maximize the QoE under any preference through automatically tuning the adaptation logic of the streaming algorithms. Evaluation results show that the QoE achieved by PSQA is substantially better than the existing approaches and in some scenarios even close to optimal. Moreover, PSQA can be readily implemented into real streaming platforms, offering a practical and reliable solution for high-performance streaming services.

Automated summary

Video streaming has become a major application on the Internet, but existing algorithms fail to efficiently improve Quality-of-Experience (QoE) due to the differing preferences of viewers. This study introduces a new framework called Post Streaming Quality Analysis (PSQA) to automatically tune streaming algorithms and maximize QoE under any preference. Evaluation results demonstrate that the PSQA significantly outperforms existing approaches and even achieves near-optimal performance in some scenarios. Additionally, the PSQA can be easily implemented into real streaming platforms, providing a practical and reliable solution for high-performance streaming services.