Dynamic resolution switching for live streaming

2026-05-15

AI Summary

This work addresses the limitations of existing adaptive bitrate streaming systems, which rely on static bitrate ladders and objective quality metrics ill-suited for live scenarios, leading to inaccurate resolution switching decisions. To overcome this, the authors propose a Dynamic Resolution Switching (DRS) framework that, while remaining compatible with current streaming protocols, dynamically constructs content-adaptive bitrate ladders in real time by leveraging user bandwidth distributions and resolution crossover regions. For the first time, a lightweight stream quality assessment model optimized for subjective switching-point prediction is integrated to guide adaptation decisions. The proposed method achieves approximately 9% BD-rate gain over baseline approaches while maintaining low latency and practicality for live streaming, substantially improving quality-efficiency trade-offs.

Abstract

Conventional adaptive bitrate (ABR) streaming systems typically rely on static bitrate ladders to optimize Quality of Experience (QoE). While operationally simple, this"one-size-fits-all"approach neglects content-specific characteristics, often compromising streaming efficiency. Per-title optimization methods address this by predicting the rate-distortion convex hull directly from the source content, but their reliance on pre-encoding source analysis can limit their applicability to live streaming. Moreover, the objective video quality metrics (VQMs) they rely on are optimized for overall correlation with subjective scores rather than cross-over accuracy, often yielding inaccurate cross-over predictions and suboptimal ladder construction. To overcome both limitations, we introduce a Dynamic Resolution Switching (DRS) framework for live streaming that remains fully compatible with existing streaming protocols. Our approach augments static ladders with strategically selected representations guided by user bandwidth distributions and cross-over regions. The quality of these representations is then analyzed in real time to construct dynamic ladders. Central to this framework is a lightweight, bitstream-based VQM that ensures computational efficiency while maximizing the accuracy of subjective resolution cross-over prediction through training on Pairwise Comparison (PC) datasets. At each bitrate, the VQM evaluates all candidate representations to identify the resolution maximizing the quality score. This decision process, operating at a configurable granularity (e.g., per segment), drives the dynamic resolution switching mechanism specifically optimized for the metric. Experimental results validate the approach, demonstrating a significant performance gain (approximately 9% BD-rate reduction under the proposed VQM) while maintaining practical feasibility for live streaming.

Problem

• adaptive bitrate streaming
• live streaming
• rate-distortion optimization
• video quality metrics
• resolution switching

Innovation

• Dynamic Resolution Switching
• Adaptive Bitrate Streaming
• Video Quality Metric
• Live Streaming
• Rate-Distortion Optimization