Cisco Technology, Inc.

Dan Tan

John George Apostolopoulos

Xiaoqing Zhu

Herbert Michael Wildfeuer

Rob Liston

Mehdi Nikkhah

Ashish Khisti

Ahmed Atef Younes Badr

Daniel George Wing

Michael Anthony Ramalho

Paul G Bright-Thomas

Geir Sandbakken

Pål-Erik Martinsen

Greg Harald Hakonsen

Jon Michael Snyder

Ahmed Badr

Yaqi Wang

In one embodiment, a service receives machine learning-based generative models from a plurality of distributed sites. Each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model. The service receives, from each of the distributed sites, a subset of labeled data observed at that site. The service uses the generative models to generate synthetic unlabeled data. The service trains a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

Communication efficient machine learning of data across multiple sites

In one embodiment, a device determines locations of a plurality of transmitters relative to a particular wireless access point in a wireless network. One of the transmitters comprises a target client to which the particular wireless access point is to communicate. The device compares a plurality of beamforming patterns associated with the particular wireless access point to the determined locations. The device selects, based on the comparison, one of the beamforming patterns for use by the particular wireless access point to communicate with the target client. The device controls the particular wireless access point to use the selected beamforming pattern to communicate with the target client.

Wireless beamforming optimization using client location information

In one embodiment, a device in a network assigns packets from a communication transmitted via the network to time windows over a period of time. The device determines a transmission performance metric for each of the packets in a particular time window and calculates, for each of the time windows, local disturbance scores, which are based on the transmission performance metrics for the packet in the time windows. A particular local disturbance score for a particular time window maps the transmission performance metrics for the packets in the time window to a perceived quality metric. The device determines a distortion score for the communication by aggregating the local disturbance scores for the time windows over the period of time.

Content-independent evaluation of streaming media based on packet transmission performance

Large source data packets having large packet sizes and small source data packets having small packet sizes that are smaller than the large packet sizes are received. The small source data packets and the large source data packets are sent to a receiving device without forward error correction (FEC). The small source data packets are aggregated into a container packet having a header configured to differentiate the container packet from the large source data packets and the small source data packets. The large source data packets and the container packet are encoded with forward error correction to produce FEC-encoded packets to enable forward error correction of the large source data packets and the container packet at the receiving device. The FEC-encoded packets are sent to the receiving device.

Efficient error correction that aggregates different media into encoded container packets

In one embodiment, a first device in a network sends a Session Traversal Utilities for Network Address Translation (STUN) binding request towards an endpoint device of a media session between the first and endpoint devices. The binding request includes one or more network attribute fields. The first device receives a binding response from an intermediate node between the first and endpoint devices in the network, in response to sending the binding request towards the endpoint device. The intermediate node inserted the one or more network attribute fields into the binding response. The received binding response includes one or more metrics for the media session in the one or more network attribute fields. The first device adjusts one or more bitrates of the media session based on the one or more metrics for the media session in the received binding response.

Optimizing media bitrate with explicit network feedback on one client only

An encoder receives a sequence of packets. For each packet, the encoder selects a window of at least previous packets in the sequence of packets. The encoder identifies in the window one or more earlier packets among the previous packets and one or more later packets separated from the one or more earlier packets by a gap including gap packets. The encoder encodes the one or more earlier packets and the one or more later packets into a forward error correction (FEC) packet corresponding to the packet, without using any of the gap packets, and transmits the FEC packet and the packet.

Forward error correction for low-delay recovery from packet loss

In one embodiment, a device in a network identifies delay requirements of each of a plurality of media streams. The device selects a joint forward error correction (FEC) encoding strategy for the plurality of media streams based on the identified delay requirements of the streams and on a burst loss length of a communication channel. The device applies the selected joint FEC encoding strategy to the plurality of media streams, to form a multiplexed packet stream. The device sends the multiplexed packet stream to one or more nodes in the network via the communication channel.

Growing community of inventors

Sunnyvale, CA, United States of America

Dan Tan