  Maximizing quality of experience (QoE) for interactive video streaming has
been a long-standing challenge, as its delay-sensitive nature makes it more
vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has
demonstrated great potential, existing works are either limited by fixed models
or require enormous data/time for online adaptation, which struggle to fit
time-varying and diverse network states. Driven by these practical concerns, we
perform large-scale measurements on WeChat for Business's interactive video
service to study real-world network fluctuations. Surprisingly, our analysis
shows that, compared to time-varying network metrics, network sequences exhibit
noticeable short-term continuity, sufficient for few-shot learning
requirements. We thus propose Fiammetta, the first meta-RL-based bitrate
adaptation algorithm for interactive video streaming. Building on the
short-term continuity, Fiammetta accumulates learning experiences through
offline meta-training and enables fast online adaptation to changing network
states through a few gradient updates. Moreover, Fiammetta innovatively
incorporates a probing mechanism for real-time monitoring of network states,
and proposes an adaptive meta-testing mechanism for seamless adaptation. We
implement Fiammetta on a testbed whose end-to-end network follows the
real-world WeChat for Business traces. The results show that Fiammetta
outperforms prior algorithms significantly, improving video bitrate by
3.6%-16.2% without increasing stalling rate.
