HTTP adaptive streaming (HAS) is increasingly adopted by over-the-top (OTT)-based video streaming services, it allows clients to dynamically switch among various stream representations. Each of these representations is encoded to target a specific bitrate providing a wide range of operating bitrates known as the bitrate ladder. Several approaches with different levels of complexity are currently used to build such a bitrate ladder. The most straightforward method is to use a fixed bitrate ladder for all videos, which is a set of bitrateresolution pairs, called "one-size-fits-all", and the most complex is based on the intensive encoding of all resolutions over a wide bitrate range to construct the convex-hull. This latter is then used to obtain a per-title bitrate ladder. Recently, various methods relying on machine learning (ML) techniques have been proposed to predict content-based ladder without performing exhaustive search encoding. In this paper, we conduct a benchmark study of several handcrafted-and deep learning (DL)-based approaches for predicting content-optimized bitrate ladder, which we believe provides baseline methods and will be useful for future research in this field. The obtained results, based on 200 video sequences compressed with the high-efficiency video coding (HEVC) encoder, reveal that the most efficient method predicts the bitrate ladder without performing any encoding process at the cost of a slight Bjøntegaard delta bitrate (BD-BR) loss of 1.43% compared to the exhaustive approach. The dataset and the source code of the considered methods are made publicly available at: https://github.com/atelili/Bitrate-Ladder-Benchmark.