arXiv:2504.17938v1 Announce Type: new
Abstract: The Quality of Experience (QoE) is the users satisfaction while streaming a video session over an over-the-top (OTT) platform like YouTube. QoE of YouTube reflects the smooth streaming session without any buffering and quality shift events. One of the most important factors nowadays affecting QoE of YouTube is frequent shifts from higher to lower resolutions and vice versa. These shifts ensure a smooth streaming session; however, it might get a lower mean opinion score. For instance, dropping from 1080p to 480p during a video can preserve continuity but might reduce the viewers enjoyment. Over time, OTT platforms are looking for alternative ways to boost user experience instead of relying on traditional Quality of Service (QoS) metrics such as bandwidth, latency, and throughput. As a result, we look into the relationship between quality shifting in YouTube streaming sessions and the channel metrics RSRP, RSRQ, and SNR. Our findings state that these channel metrics positively correlate with shifts. Thus, in real-time, OTT can only rely on them to predict video streaming sessions into lower- and higher-resolution categories, thus providing more resources to improve user experience. Using traditional Machine Learning (ML) classifiers, we achieved an accuracy of 77-percent, while using only RSRP, RSRQ, and SNR. In the era of 5G and beyond, where ultra-reliable, low-latency networks promise enhanced streaming capabilities, the proposed methodology can be used to improve OTT services.
The Impact of Quality Shifting on YouTube Streaming Sessions
In the increasingly digital world we live in, the demand for high-quality streaming services has skyrocketed. As users turn to platforms like YouTube to consume video content, their satisfaction, known as Quality of Experience (QoE), becomes a key factor in their overall viewing experience. In this context, it is essential to understand how the quality shifting phenomenon affects QoE, and how it can be optimized to enhance user satisfaction.
Traditionally, QoS metrics such as bandwidth, latency, and throughput have been used to assess streaming performance. However, as the article points out, these metrics alone are no longer sufficient to measure QoE accurately. This is where the concept of quality shifting comes into play. By dynamically adjusting video quality during a streaming session, platforms like YouTube can ensure a smooth viewing experience without buffering interruptions. However, this practice can also impact viewer enjoyment. For example, sudden shifts from higher to lower resolutions can lead to a decrease in satisfaction.
The study discussed in the article delves into the relationship between quality shifting in YouTube streaming sessions and specific channel metrics: RSRP, RSRQ, and SNR. These metrics, which are related to signal strength and quality, were found to positively correlate with shifts. In other words, they can serve as indicators to predict when a video streaming session might transition between lower and higher resolutions. By leveraging this information in real-time, over-the-top (OTT) platforms can allocate appropriate resources to improve user experience.
The researcher’s utilization of traditional Machine Learning (ML) classifiers and the achievement of a 77% accuracy rate using only RSRP, RSRQ, and SNR is a significant finding. This demonstrates the potential of using predictive algorithms to enhance QoE by proactively managing quality shifts in streaming sessions.
In the wider field of multimedia information systems, this research has important implications. As the demand for high-quality video content continues to rise and technologies such as 5G promise enhanced streaming capabilities, finding innovative ways to optimize QoE becomes imperative. By combining insights from multiple disciplines, including computer science, telecommunications, and human-computer interaction, this study contributes to improving the overall streaming experience for users.
Beyond YouTube, the concepts discussed in this article also have implications for other forms of multimedia, such as animations, artificial reality, augmented reality, and virtual realities. These immersive multimedia experiences heavily rely on streaming technologies, and ensuring a smooth and uninterrupted experience is crucial for user engagement. By further exploring the relationship between quality shifting and user satisfaction, researchers can develop innovative solutions to enrich multimedia experiences across various platforms and applications.
Conclusion
The study presented in this article highlights the impact of quality shifting on YouTube streaming sessions and its relationship with channel metrics such as RSRP, RSRQ, and SNR. By leveraging these metrics and utilizing machine learning techniques, OTT platforms can predict quality shifts in real-time and allocate appropriate resources to enhance user experience. The multi-disciplinary nature of this research, spanning areas like multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, makes it a valuable contribution to the field. As technologies evolve and demand for high-quality streaming services grows, innovative approaches like those presented in this study will play a crucial role in delivering an optimal multimedia experience.
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