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Content delivery of live streams with event-adaptive encoding

專利號
US10897654B1
公開日期
2021-01-19
申請人
Amazon Technologies, Inc.(US WA Seattle)
發(fā)明人
Hai Wei; Yongjun Wu; Abhishek Kumar; Lei Li; Amarsingh B. Winston
IPC分類
H04N21/647; H04N21/2343; H04N21/238; H04L12/811; H04N19/50; H04L29/06
技術(shù)領(lǐng)域
encoding,live,trial,profiles,video,ladder,adaptive,parameter,bit,content
地域: WA WA Seattle

摘要

Techniques are described for optimizing event-adaptive live video encoding profiles.

說明書

In a class of implementations, the event-adaptive encoding ladder optimization includes encoding the representative segments with trial encode settings (306). The trial encode settings may be those of trial encoding profiles 155, as an example. Encoding the representative segments with trial encode settings may include data point generation for a multidimensional data space. Examples of dimensions of the data space include any combination of bit rate, quality, encoding runtime, network bandwidth, playback device limitations, end-user distribution, and geographic constraints, etc. It should be appreciated that the dimensions of the data space may correspond to system constraints for the design of a live video streaming service.

The trial encode settings can span a wide range of content encoding parameters. Examples of encoding parameters include but are not limited to one or more of a Group Of Pictures (GOP) structure, entropy coding mode (Context-adaptive binary arithmetic coding (CABAC)/Context-adaptive variable-length coding (CAVLC)), number of reference frames, interlace coding support, motion search range, motion search algorithm, intra/inter encoding partition size, intra/inter encoding partition size mode decision, intra prediction mode decision, Picture-adaptive frame-field coding (PAFF) decisions, Macroblock-adaptive frame-field (MBAFF) coding decisions, macro block (MB) level quantization parameters, deblocking filter strength, quantization scaling matrices, quantization rounding offsets, Lagrangian multipliers for rate-distortion (RD) optimization, various thresholds to bias mode decision, adaptive-quantization mode, adaptive-quantization strength, constant rate factor, inter noise reduction level, quantization group size, sub-pixel motion estimation method, quantization approach, discrete cosine transform (DCT) size, the use of mixed references, and so forth.

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