Amazon Technologies, Inc.

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Deepthi Nandakumar

Lei Li

Amarsingh Buckthasingh Winston

Hai Wei

Yang Yang

Charles Benjamin Waggoner

Brian Manuel Alvarez

Snehith Alapati

Daniel Robert Graham

Techniques for resolution-based manifest generation for adaptive bitrate video streaming are described. As one example, a computer-implemented method includes performing a first, convex hull optimization on a video file for a first device resolution, performing a second, convex hull optimization on the video file for a second, lower device resolution, generating a first video representation for a point on both the first, convex hull optimization and the second, convex hull optimization for the first device resolution and the second, lower device resolution, generating a second video representation for a point only on the second, convex hull optimization for the second, lower device resolution, receiving a request for a manifest for the video file from a client device at the second, lower device resolution, generating the manifest for the client device that identifies the first video representation and the second video representation, and sending the manifest to the client device.

Resolution-based manifest generator for adaptive bitrate video streaming

A video may include a runtime. Based on analysis of the video, a scene or shot change may be generated based on the content of the video. For instance, the video may depict a character inside of a house in a first shot and, in a subsequent shot, the video may depict the character as being outside in a forest. A shot change may indicate the transition from the character being depicted inside in the first shot and outside in the second shot at a period of time during the runtime of the video. The video may be fragmented into fragments based on the shot change and based on a minimum or maximum fragment duration. When a video is fragmented into fragments, a key frame that is independently decodable may be inserted into a fragment. Encoding settings may then be determined based on the key frame.

Optimal fragmentation of video based on shot analysis

Encoding of a video file includes determining a plurality of scenes associated with a video file, and determining at least one group of pictures (GOP). Starting sequentially from a beginning frame of the video file, the system identifies a first GOP having a first encoding error characteristic. The system changes a bitrate allocation setting from a first setting to a second setting based on the encoding error characteristic. The system identifies a second frame having a second encoding error characteristic, and changes a second bitrate allocation setting from the second setting to a third setting based on the second encoding error characteristic. The system generates an encoded video file that includes an encoded plurality of scenes.

Adaptive group of pictures (GOP) encoding

Techniques are described for optimizing streaming video encoding profiles.

Optimizing streaming video encoding profiles

Methods and apparatus are described in which both supervised and unsupervised machine learning are used to classify video content for compression using encoding profiles that are optimized for each type of video content.

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Bothell, WA, United States of America

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