Optimizing RTC bandwidth estimation with machine learning

Our existing bandwidth estimation (BWE) module at Meta is based on WebRTC’s Google Congestion Controller (GCC) . We have made several improvements through parameter tuning, but this has resulted in a more complex system, as shown in Figure 1.

One challenge with the tuned congestion control (CC)/BWE algorithm was that it had multiple parameters and actions that were dependent on network conditions. For example, there was a trade-off between quality and reliability; improving quality for high-bandwidth users often led to reliability regressions for low-bandwidth users, and vice versa, making it challenging to optimize the user experience for different network conditions.

To solve these inefficiencies, we developed a machine learning (ML)-based, network-targeting approach that offers a cleaner alternative to hand-tuned rules. This approach also allows us to solve networking problems holistically across cross-layers such as BWE, network resiliency, and transport.

An ML model-based approach leverages time series data to improve the bandwidth estimation by using offline parameter tuning for characterized network types. 

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