Camel: Frame-Level Bandwidth Estimation for Low-Latency Live Streaming under Video Bitrate Undershooting

2026-02-10

AI Summary

This work addresses the persistent stuttering in low-latency live streaming, which occurs even when the encoded bitrate remains below available bandwidth, due to the inability of traditional packet-level congestion control to accurately estimate available bandwidth amid video frame encoding fluctuations. To resolve this, the authors propose Camel—the first frame-level congestion control algorithm tailored for low-latency live streaming—that decouples encoding-induced variability from bandwidth estimation using frame-level network feedback and introduces a burst-length control mechanism to dynamically optimize both average sending rate and burst patterns. The system comprises three core modules: a bandwidth/delay estimator, a congestion detector, and a burst controller. Deployed on a platform serving hundreds of millions of users, Camel increased 1080p stream share by 70.8%, raised media bitrate by 14.4%, and reduced stuttering by 14.1%; simulations further demonstrated up to 93.0% stutter reduction and a 23.9% improvement in bandwidth estimation accuracy.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Online Learning & Bandits
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Low-latency live streaming (LLS) has emerged as a popular web application, with many platforms adopting real-time protocols such as WebRTC to minimize end-to-end latency. However, we observe a counter-intuitive phenomenon: even when the actual encoded bitrate does not fully utilize the available bandwidth, stalling events remain frequent. This insufficient bandwidth utilization arises from the intrinsic temporal variations of real-time video encoding, which cause conventional packet-level congestion control algorithms to misestimate available bandwidth. When a high-bitrate frame is suddenly produced, sending at the wrong rate can either trigger packet loss or increase queueing delay, resulting in playback stalls. To address these issues, we present Camel, a novel frame-level congestion control algorithm (CCA) tailored for LLS. Our insight is to use frame-level network feedback to capture the true network capacity, immune to the irregular sending pattern caused by encoding. Camel comprises three key modules: the Bandwidth and Delay Estimator and the Congestion Detector, which jointly determine the average sending rate, and the Bursting Length Controller, which governs the emission pattern to prevent packet loss. We evaluate Camel on both large-scale real-world deployments and controlled simulations. In the real-world platform with 250M users and 2B sessions across 150+ countries, Camel achieves up to a 70.8% increase in 1080P resolution ratio, a 14.4% increase in media bitrate, and up to a 14.1% reduction in stalling ratio. In simulations under undershooting, shallow buffers, and network jitter, Camel outperforms existing congestion control algorithms, with up to 19.8% higher bitrate, 93.0% lower stalling ratio, and 23.9% improvement in bandwidth estimation accuracy.

Problem

• low-latency live streaming
• bitrate undershooting
• bandwidth estimation
• congestion control
• frame-level encoding

Innovation

• frame-level congestion control
• bandwidth estimation
• low-latency live streaming
• bitrate undershooting
• burst-aware sending