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HTTP adaptive streaming (HAS) has emerged as a prevalent approach forover-the-top (OTT) video streaming services due to its ability to deliver aseamless user experience. A fundamental component of HAS is the bitrate ladder,which comprises a set of encoding parameters (e.g., bitrate-resolution pairs)used to encode the source video into multiple representations. This adaptivebitrate ladder enables the client's video player to dynamically adjust thequality of the video stream in real-time based on fluctuations in networkconditions, ensuring uninterrupted playback by selecting the most suitablerepresentation for the available bandwidth. The most straightforward approachinvolves using a fixed bitrate ladder for all videos, consisting ofpre-determined bitrate-resolution pairs known as one-size-fits-all. Conversely,the most reliable technique relies on intensively encoding all resolutions overa wide range of bitrates to build the convex hull, thereby optimizing thebitrate ladder by selecting the representations from the convex hull for eachspecific video. Several techniques have been proposed to predict content-basedladders without performing a costly, exhaustive search encoding. This paperprovides a comprehensive review of various convex hull prediction methods,including both conventional and learning-based approaches. Furthermore, weconduct a benchmark study of several handcrafted- and DL-based approaches forpredicting content-optimized convex hulls across multiple codec settings. Theconsidered methods are evaluated on our proposed large-scale dataset, whichincludes 300 UHD video shots encoded with software and hardware encoders usingthree state-of-the-art video standards, including AVC/H.264, HEVC/H.265, andVVC/H.266, at various bitrate points. Our analysis provides valuable insightsand establishes baseline performance for future research in this field.

Convex Hull Prediction Methods for Bitrate Ladder Construction: Design, Evaluation, and Comparison