  The quality of experience (QoE) delivered by video conferencing systems to
end users depends in part on correctly estimating the capacity of the
bottleneck link between the sender and the receiver over time. Bandwidth
estimation for real-time communications (RTC) remains a significant challenge,
primarily due to the continuously evolving heterogeneous network architectures
and technologies. From the first bandwidth estimation challenge which was
hosted at ACM MMSys 2021, we learned that bandwidth estimation models trained
with reinforcement learning (RL) in simulations to maximize network-based
reward functions may not be optimal in reality due to the sim-to-real gap and
the difficulty of aligning network-based rewards with user-perceived QoE. This
grand challenge aims to advance bandwidth estimation model design by aligning
reward maximization with user-perceived QoE optimization using offline RL and a
real-world dataset with objective rewards which have high correlations with
subjective audio/video quality in Microsoft Teams. All models submitted to the
grand challenge underwent initial evaluation on our emulation platform. For a
comprehensive evaluation under diverse network conditions with temporal
fluctuations, top models were further evaluated on our geographically
distributed testbed by using each model to conduct 600 calls within a 12-day
period. The winning model is shown to deliver comparable performance to the top
behavior policy in the released dataset. By leveraging real-world data and
integrating objective audio/video quality scores as rewards, offline RL can
therefore facilitate the development of competitive bandwidth estimators for
RTC.
