Karma: Adaptive Video Streaming via Causal Sequence Modeling
Bowei Xu Hao Chen Zhan Ma
Optimal adaptive bitrate (ABR) decision depends on a comprehensive
characterization of state transitions that involve interrelated modalities over
time including environmental observations, returns, and actions. However,
state-of-the-art learning-based ABR algorithms solely rely on past observations
to decide the next action. This paradigm tends to cause a chain of deviations
from optimal action when encountering unfamiliar observations, which
consequently undermines the model generalization. This paper presents Karma, an
ABR algorithm that utilizes causal sequence modeling to improve generalization
by comprehending the interrelated causality among past observations, returns,
and actions and timely refining action when deviation occurs. Unlike direct
observation-to-action mapping, Karma recurrently maintains a multi-dimensional
time series of observations, returns, and actions as input and employs causal
sequence modeling via a decision transformer to determine the next action. In
the input sequence, Karma uses the maximum cumulative future quality of
experience (QoE) (a.k.a, QoE-to-go) as an extended return signal, which is
periodically estimated based on current network conditions and playback status.
We evaluate Karma through trace-driven simulations and real-world field tests,
demonstrating superior performance compared to existing state-of-the-art ABR
algorithms, with an average QoE improvement ranging from 10.8% to 18.7% across
diverse network conditions. Furthermore, Karma exhibits strong generalization
capabilities, showing leading performance under unseen networks in both
simulations and real-world tests.