Machine Learning-Based Prediction of Quality Shifts on Video Streaming Over 5G

2025-04-24

AI Summary

Frequent video resolution switches (e.g., 1080p ↔ 480p) in 5G video streaming—triggered by wireless channel fluctuations—severely degrade user-perceived quality of experience (QoE). Method: This work establishes, for the first time, strong correlations between three physical-layer channel metrics—Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Noise Ratio (SNR)—and categories of video quality jumps. It proposes a lightweight, real-time prediction framework that uses only these three-dimensional channel measurements reported by user equipment as input, employing classifiers such as SVM and Random Forest. Contribution/Results: The framework achieves 77% accuracy in predicting quality jump categories—without relying on end-to-end QoS metrics—enabling ultra-low-latency, low-overhead QoE optimization. It provides a deployable, measurement-driven paradigm for adaptive scheduling of OTT streaming in 5G networks.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Motion & Tracking

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• Predicting video quality shifts in 5G streaming using ML
• Analyzing RSRP/RSRQ/SNR impact on YouTube QoE
• Improving OTT user experience beyond traditional QoS metrics

Innovation

• Machine Learning predicts video quality shifts
• Uses RSRP, RSRQ, SNR for real-time prediction
• Achieves 77% accuracy with channel metrics