One of the biggest challenges in modern-era streaming is the fragmentation of codec support across receiving devices. For example, modern Apple devices can decode and seamlessly switch between H.264/AVC and HEVC streams. Most new TVs and set-top boxes can also decode HEVC, but they cannot switch between HEVC and H.264/AVC streams. And there are still plenty of older devices/streaming clients that can only receive and decode H.264/AVC streams. With the arrival of next-generation codecs - such as AV1 and VVC, the fragmentation of codec support across devices becomes even more complex. This situation brings a question – how we can serve such a population of devices most efficiently by using codecs delivering the best performance in all cases yet producing the minimum possible number of streams and such that the overall cost of media delivery is minimal? In this paper, we explain how this problem can be formalized and solved at the stage of dynamic generation of encoding profiles for ABR streaming. The proposed solution is a generalization of contextaware encoding (CAE) class-of techniques, considering multiple sets of renditions generated using each codec and codec usage distributions by the population of the receiving devices. We also discuss several streaming system-level tools needed to make the proposed solution practically deployable.
In a multi-generation transcoding system, the source may be an encoded mezzanine video whose objective video quality metrics (e.g., PSNR, SSIM) are unknown. Transcoding process yields objective quality metrics that are relative to the encoded source video, which does not indicate the actual quality of the transcoded video relative to the original uncompressed reference video. In this paper, we present an approach for estimating the objective quality metrics of the encoded mezzanine and demonstrate that it has higher accuracy compared to a well-known scheme. Finally, we derive bounds for the end-to-end objective quality metrics of the transcoded video, and use it for controlling the transcoding process to ensure that the final transcoded video satisfies a quality criterion.
We discuss a problem of optimal design of encoding profiles for adaptive bitrate (ABR) streaming applications.
We show, that under certain conditions and optimization targets, this problem becomes equivalent to the problem of quantization of random variable, which in this case is bandwidth of a communication channel between streaming server and the client. But using such reduction to a known information-theoretic problem, we immediately arrive at class of algorithms for solving this problem optimally. We illustrate effectiveness of our approach by examples of optimal encoding ladders designed for different networks and reproduction devices.
Specific techniques and models utilized in this paper include:
- modeling of SSIM-rate functions for modern video codecs (H.264, HEVC) and different content
- adaptation of SSIM (by using scaling & CSF-filteing) to account for different resolutions and reproduction settins
- SSIM - MOS scale mapping
- CDF models of typical communication networks (wireless, cable, WiFi, etc)
- algorithms for solving quantization problem (Lloyd-Max algorithms, analytic solutions, etc)
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