Content-adaptive Encoder Preset Prediction for Adaptive Live Streaming

TL;DR

This paper presents CAPS, a content-adaptive scheme that predicts optimal encoder presets for live streaming, improving quality and CPU utilization by analyzing video content and encoding parameters.

Contribution

It introduces a novel content-adaptive preset prediction method for live streaming that enhances quality and efficiency over fixed preset approaches.

Findings

Achieves 0.83 dB PSNR improvement at same bitrate

Yields 3.81 VMAF score increase

Maintains encoding speed while reducing CPU idle time

Abstract

In live streaming applications, a fixed set of bitrate-resolution pairs (known as bitrate ladder) is generally used to avoid additional pre-processing run-time to analyze the complexity of every video content and determine the optimized bitrate ladder. Furthermore, live encoders use the fastest available preset for encoding to ensure the minimum possible latency in streaming. For live encoders, it is expected that the encoding speed is equal to the video framerate. An optimized encoding preset may result in (i) increased Quality of Experience (QoE) and (ii) improved CPU utilization while encoding. In this light, this paper introduces a Content-Adaptive encoder Preset prediction Scheme (CAPS) for adaptive live video streaming applications. In this scheme, the encoder preset is determined using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for every video segment, the number of CPU threads allocated for each encoding instance, and the target encoding speed. Experimental results show that CAPS yields an overall quality improvement of 0.83 dB PSNR and 3.81 VMAF with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder. This is achieved by maintaining the desired encoding speed and reducing CPU idle time.