KEYWORDS: Video coding, Video, Scalable video coding, Video processing, Model based design, Detection and tracking algorithms, Video compression, Data modeling, Visualization
In the video streaming service for VOD use case, an important task is to transcode user uploaded videos into multiple encoded bitstreams at different encoding bitrates and encoding resolutions, which allows the client player to leverage ABR (adaptive bitrate) algorithm to select the bitstream segments based on its available bandwidth. In this workflow, the key decision needs to be made is to determine the optimal encoding bitrates and encoding resolutions for every video at each quality or bitrate target in a ABR ladder. To tackle this challenge, an efficient two-stage convex hull based dynamic optimization framework was recently proposed. In this two-stage processing flow, two different encoders, or encoder presets can be used to construct the convex hull to improve the computation efficiency. In this work, we study the cross codec encoding parameter prediction problem in the two-stage system. We first formulate the prediction into an optimization problem, then propose two methods towards this optimization with validation results. We also discuss some potential directions that can further improve the results.
KEYWORDS: Video coding, Video, Power consumption, Energy efficiency, Video compression, Open source software, Scalable video coding, Video processing, Clocks
In this paper, we present a methodology for benchmarking the coding efficiency and energy efficiency of software and hardware video transcoding implementations. This study builds upon our previous work, which focused on software encoders such as x264, x265, libvpx, vvenc, and SVT-AV1. We have since added a closed-source video software encoder implementation, EVE-VP9, as well as Meta’s MSVP VP9 encoder as a hardware representative, and expanded the test set to include a wider variety of test content in our analysis. To ensure a fair comparison between software and hardware encoders, we normalize the video encoding efficiency to energy used in watt-hours. Our proposed test methodology includes a detailed description of the process for measuring compression efficiency and energy consumption. We summarize limitations of our methodology and identify future opportunities for improvement.
Videos uploaded to Meta's Family-of-Apps are transcoded into multiple bitstreams of various codec formats, resolutions and quality to provide the best video quality across the wide variety of devices and connection bandwidth constraints. On Facebook alone, there are more than 4 billion video views per day and to address the video processing at this scale, we needed a video processing solution that can deliver the best video quality possible, with the shortest amount of encoding time — all while being energy efficient, programmable, and scalable. In this paper, we present, Meta Scalable Video Processor (MSVP) that can do video processing at on-par quality compared to SW solutions but at a small fraction of the compute time and energy. Each MSVP ASIC can offer a peak SIMO (Single Input Multiple Output) transcoding performance of 4K at 15fps at the highest quality configuration and can scale up to 4K at 60fps at the standard quality configuration. This performance is achieved at ~10W of PCIe module power. We achieved a throughput gain of ~9x for H.264 when compared against libx264 SW encoding. For VP9, we achieved a throughput gain of ~50x when compared with libVPX speed 2 preset. Key components of MSVP transcoding include video decode, scalar, encoding and quality metric computation. In this paper, we go over ASIC architecture of MSVP, design of individual components and compare the perf/W vs quality against standard industry used SW encoders.
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