Apple Inc.

Cisco Technology, Inc.

Zheng Lu

Srinivasan Nimmala

Vijay Venkataraman

Sethuraman Gurumoorthy

Srirang Ashok Lovlekar

Murtaza A Shikari

Muthukumaran Dhanapal

Lakshmi N Kavuri

Sridhar Prakasam

Viswanath Nagarajan

Xiangpeng Jing

Pankaj Subhash Vasandani

Robert K Kitchens

John T Chapman

Alon Shlomo Bernstein

Xiaoqing Zhu

Sangeeta Ramakrishnan

Dantong Liu

Elias Chavarria Reyes

A device and method for a user equipment (UE) to implement power savings mechanisms when operating within a significant location. The UE identifies that the UE is at a significant location. The significant location is a location relative to a currently camped cell of a network. At a first time and at the significant location, the UE performs a type of operation related to the network connection. The UE stores information corresponding to the performance of the type of operation at the first time in a profile associated with the significant location. At a second time and at the significant location, the UE performs the type of operation related to the network connection. The performance of the type of operation at the second time is modified based on the information stored in the profile associated with the significant location.

Power efficient operation at significant locations

Predictive scheduling may be provided. First, a first device may identify when a service flow is expected to become active. The first device may estimate an initial traffic profile in response to identifying when the service flow is expected to become active. The first device may then grant allocation based on the initial traffic profile of the service flow. Next, the first device may collect feedback to later update the traffic profile estimate. The first device may then update the traffic profile estimate.

Predictive scheduler

In one implementation, a method performed by a client device includes: determining a perceptual characterization metric, where the perceptual characterization metric provides a quantitative characterization of factors that determine quality of experience associated with a first playback profile of media content presented by the client device; and normalizing the perceptual characterization metric based on characterization factors associated with the client device. The method further includes: obtaining an aggregate perceptual characterization metric that is based at least in part on the normalized perceptual characterization metric from the client device and one or more other perceptual characterization metrics from other client devices sharing at least one network resource with the client device; and determining whether to change the first playback profile based on a function of the first normalized perceptual characterization metric and the aggregate perceptual characterization metric.

Method and device for network-assisted client-centric QoE optimization

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Milpitas, CA, United States of America

Zheng Lu