Content Adaptive Encoding For Interactive Game Streaming
This addresses the problem of improving video quality for interactive game streaming services under strict latency and compute constraints, representing a novel application of CAE in a challenging domain.
The paper tackled the challenge of applying content-adaptive encoding to interactive game streaming by proposing a CNN-based method that uses past frame metadata to adapt resolution, resulting in a 2.3-point improvement in VMAF quality over fixed-resolution encoding with no latency overhead.
Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead.