Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Mohan Padmanabhan

Satheesh Ramalingam

Amarsingh Buckthasingh Winston

Rahul Urgaonkar

Manish Rao

Adhvik Arun Shetty

Moping Dou

Hargun Kaur

Yongjun Wu

Mayukh Ray

Zahaib Akhtar

Aditya Kekatpure

Tianyu Chen

Adaptive bitrate techniques are used to select bitrates of segments to download when streaming video content. Different bitrate paths may be evaluated to optimize an objection function. Filtering bitrate paths that are unlikely to be optimal increases the computational speed, facilitating deployment.

Adaptive video streaming algorithm for optimized model predictive control

Systems, methods, and computer-readable media are disclosed for optimizing network performance on a computer device to improve quality of experience by determining which network settings on the computing device to adjust. A clustering algorithm may identify various classes of networks and a classification algorithm may determine a network class specific to a network on a computing device. The effects of certain network settings for that networks class may be determined and the network setting and/or settings that optimizes the network performance may be promoted. The system may periodically analyze network data to recalculate the appropriate networks class and may determine different network settings based on the recalculation, facilitating mid-session improvements to the quality of experience.

Machine learning-based playback optimization using network-wide heuristics

Systems and methods for optimizing network settings

In non-limiting examples of the present disclosure, systems, methods and devices for transmitting data stored in a source data store to a destination data store are presented. A plurality of data chunks may be received in a first format. At least one upsert operation may be performed on each of the data chunks for converting the first format to a format compatible with a destination data store schema. A transfer of the upserted data chunks to the destination data store may be executed. A determination may be made that at least one of the upserted data chunks has failed to transfer to the destination data store. Failed data chunks may be stored in a failed chunk retry data store. The at least one upsert operation may be re-performed on failed data chunks and a re-execution of the transfer of failed data chunks to the destination data store may be made.

Advanced retry mechanism for transmitting large datasets

Growing community of inventors

Sammamish, WA, United States of America

Mohan Padmanabhan