Evaluating HAS and Low-Latency Streaming Algorithms for Enhanced QoE

The demand for multimedia traffic over internet is exponentially growing. HTTP adaptive streaming (HAS) is the leading video delivery system that delivers high quality video to the end-user. The adaptive bitrate algorithms (ABR) running on the HTTP client select the highest feasible video quality by adjusting the quality according to the fluctuating network conditions. Recently, low-latency ABR algorithms have been introduced to reduce the end-to-end latency commonly experienced in HAS. However, a comprehensive study of the low-latency algorithms remains limited. In this paper, we present an evaluation of low-latency algorithms and compare their performance with traditional DASH-based ABR algorithms across multiple QoE metrics, various network conditions, and diverse content types. Additionally, we conduct an extensive subjective test to evaluate the impact of video quality variations on QoE. The results show that the algorithms do not perform consistently under different network conditions and content settings. The results indicate that the traditional ABR algorithms outperform low-latency algorithms in stable network conditions. When segment durations are shorter, low-latency algorithms outperform traditional ABR algorithms under variable network conditions. The findings also reveal that the performance of the algorithms drops as the segment duration increases. The dynamic algorithm performs best when the segment duration is increased and under high risk of playback interruptions.