Veritas: Answering Causal Queries from Video Streaming Traces
Chandan Bothra, Jianfei Gao, Sanjay Rao, Bruno Ribeiro

TL;DR
Veritas introduces a novel framework for causal inference in adaptive video streaming, enabling accurate counterfactual and interventional queries without randomized trials by modeling latent bandwidth and network effects.
Contribution
The paper presents Veritas, a domain-specific ML framework using Hidden Markov Models to perform causal reasoning in video streaming, addressing complex confounding factors.
Findings
Veritas accurately answers counterfactual and interventional queries.
It outperforms baseline and neural network approaches in accuracy.
Veritas achieves near-oracle performance in experiments.
Abstract
In this paper, we seek to answer what-if questions - i.e., given recorded data of an existing deployed networked system, what would be the performance impact if we changed the design of the system (a task also known as causal inference). We make three contributions. First, we expose the complexity of causal inference in the context of adaptive bit rate video streaming, a challenging domain where the network conditions during the session act as a sequence of latent and confounding variables, and a change at any point in the session has a cascading impact on the rest of the session. Second, we present Veritas, a novel framework that tackles causal reasoning for video streaming without resorting to randomised trials. Integral to Veritas is an easy to interpret domain-specific ML model (an embedded Hidden Markov Model) that relates the latent stochastic process (intrinsic bandwidth that the…
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Taxonomy
TopicsImage and Video Quality Assessment · Network Traffic and Congestion Control · Internet Traffic Analysis and Secure E-voting
