The demand for multimedia traffic over the 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 (ABR) algorithms 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. This paper investigates the effectiveness of low-latency streaming algorithms in maintaining a high quality of experience (QoE) while minimizing playback delay. We evaluate these algorithms in the context of both Dynamic Adaptive Streaming over HTTP (DASH) and the Common Media Application Format (CMAF), with a particular focus on the impact of chunked encoding and transfer mechanisms on the QoE. We perform both objective as well as subjective evaluations 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. The results demonstrate that low-latency algorithms consistently deliver high video quality across various content types and network conditions, whereas the performance of the traditional adaptive bitrate (ABR) algorithms exhibit performance variability under fluctuating network conditions and diverse content characteristics. Although traditional ABR algorithms download higher-quality segments in stable network environments, their effectiveness significantly declines under unstable conditions. Furthermore, the low-latency algorithms maintained high user experience regardless of segment duration. In contrast, the performance of traditional algorithms varied significantly with changes in segment duration. In summary, the results underscore that no single algorithm consistently achieves optimal performance across all experimental conditions. Performance varies depending on network stability, content characteristics, and segment duration, highlighting the need for adaptive strategies that can dynamically respond to varying streaming environments.

Video-centric applications have seen significant growth in recent years with HTTP Adaptive Streaming (HAS) becoming a widely adopted method for video delivery. Recently, low-latency (LL) adaptive bitrate (ABR) algorithms have recently been proposed to reduce the end-to-end delay in HTTP adaptive streaming. This study investigates whether low-latency adaptive bitrate (LL-ABR) algorithms, in their effort to reduce delay, also compromise video quality. To this end, this study presents both objective and subjective evaluation of user experience with traditional DASH and low-latency ABR algorithms. The study employs crowdsourcing to evaluate user-perceived video quality in low-latency MPEG-DASH streaming, with a particular focus on the impact of short segment durations. We also investigate the extent to which quantitative QoE (Quality of Experience) metrics correspond to the subjective evaluation results. Results show that the Dynamic algorithm outperforms the low-latency algorithms, achieving higher stability and perceptual quality. Among low-latency methods, Low-on-Latency (LOL+) demonstrates superior QoE compared to Learn2Adapt-LowLatency (L2A-LL), which tends to sacrifice visual consistency for latency gains. The findings emphasize the importance of integrating subjective evaluation into the design of ABR algorithms and highlight the need for user-centric and perceptually aware optimization strategies in low-latency streaming systems. Our results show that the subjective scores do not always align with objective performance metrics. The viewers are found to be sensitive to complex or high-motion content, where maintaining a consistent user experience becomes challenging despite favorable objective performance metrics.

The use of video streaming is constantly increasing. High-resolution video requires resources on both the sender and the receiver side. Many compression techniques can be utilized to compress the video and simultaneously maintain quality. The main goal of this paper is to provide an overview of video streaming and QoE. This paper describes the basic concepts and discusses existing methodologies to measure QoE. Subjective, objective, and video compression technologies are discussed. This review paper gathers the codec implementation developed by MPEG, Google, and Apple. This paper outlines the challenges and future research directions that should be considered in the measurement and assessment of the quality of experience for video services.

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.

Video-centric applications have seen significant growth in recent years with HTTP Adaptive Streaming (HAS) becoming a widely adopted method for video delivery. Recently, low-latency (LL) adaptive bitrate (ABR) algorithms have recently been proposed to reduce the end-to-end delay in HTTP adaptive streaming. This study investigates whether low-latency adaptive bitrate (LL-ABR) algorithms, in their effort to reduce delay, also compromise video quality. To this end, this study presents both objective and subjective evaluation of user experience with traditional DASH and low-latency ABR algorithms. The study employs crowdsourcing to evaluate user-perceived video quality in low-latency MPEG-DASH streaming, with a particular focus on the impact of short segment durations. We also investigate the extent to which quantitative QoE metrics correspond to the subjective evaluation results. Results show that the Dynamic algorithm outperforms the low-latency algorithms, achieving higher stability and perceptual quality. Among low-latency methods, LoL+ demonstrates superior QoE compared to L2A-LL, which tends to sacrifice visual consistency for latency gains. The findings emphasize the importance of integrating subjective evaluation into the design of ABR algorithms and highlight the need for user-centric and perceptually aware optimization strategies in low-latency streaming systems. Our results show that the subjective scores do not always align with objective performance metrics. The viewers are found to be sensitive to complex or high-motion content, where maintaining a consistent user experience becomes challenging despite favorable objective performance metrics.