  Contemporary real-time video communication systems, such as WebRTC, use an
adaptive bitrate (ABR) algorithm to assure high-quality and low-delay services,
e.g., promptly adjusting video bitrate according to the instantaneous network
bandwidth. However, target bitrate decisions in the network and bitrate control
in the codec are typically incoordinated and simply ignoring the effect of
inappropriate resolution and frame rate settings also leads to compromised
results in bitrate control, thus devastatingly deteriorating the quality of
experience (QoE). To tackle these challenges, Mamba, an end-to-end
multi-dimensional ABR algorithm is proposed, which utilizes multi-agent
reinforcement learning (MARL) to maximize the user's QoE by adaptively and
collaboratively adjusting encoding factors including the quantization
parameters (QP), resolution, and frame rate based on observed states such as
network conditions and video complexity information in a video conferencing
system. We also introduce curriculum learning to improve the training
efficiency of MARL. Both the in-lab and real-world evaluation results
demonstrate the remarkable efficacy of Mamba.
