In a method for exchanging packets between first and second nodes of a packet-switched network, each packet comprises two fields settable to an idle value or measurement value. The first node transmits to the second node first packets having a filed set to measurement value. Upon reception of each first packet, the second node transmits back to the first node a second packet having a field set to measurement value. Upon reception of each second packet, the first node transmits to the second node a third packet having another field set to measurement value. A packet loss measurement is calculated as a difference between the number of first packets and the number of third packets.

A cloud network is a complex environment in which hundreds and thousands of users or entities can each host, create, modify, and develop multiple virtual machines. Each virtual machine can have complex behavior unknown to the provider or maintainer of the cloud. Technologies disclosed include methods, systems, and apparatuses to monitor the complex environment to detect network anomalies using machine learning techniques. In addition, techniques to modify and adapt to user feedback are provided allowing the developed models to be tuned for specific use cases, virtual machine types, and users.

This disclosure falls into the field of voice communication systems, more specifically it is related to the field of voice quality estimation in a packet based voice communication system. In particular the disclosure provides a method and device for 5 reducing a prediction error of the voice quality estimation by considering the content of lost packets. Furthermore, this disclosure provides a method and device which uses a voice quality estimating algorithm to calculate the voice quality estimate based on an input which is switchable between a first and a second input mode.

In some examples, a computing device may determine a prediction of a network outage of a network. The computing device may determine a priority of one or more data types expected to be received during the network outage. Further, the computing device may determine a latency category of the one or more data types expected to be received during the network outage. The computing device may store a data transmission rule for the one or more data types at least partially based on the priority and the latency category. The computing device may receive, from one or more data generators, during the network outage, data for transmission to the network. The computing device may transmit at least some of the received data to the network at least partially based on the data transmission rule.

Measuring quality-of-experience (QoE) for virtual reality (VR) streaming content is disclosed. A network computing device receives a client-side VR stream capture and a client pose data set that are generated by a client computing device based on a VR content and one or more induced network impairments (e.g., latency, packet loss, and/or jitter, as non-limiting examples). Using the same VR content and the client pose data set, the network computing device generates a source VR stream capture that is not subjected to the one or more induced network impairments. The network computing device performs a frame-by-frame comparison of the client-side VR stream capture and the source VR stream capture. Based on the frame-by-frame comparison, the network computing device generates a QoE metric that indicates a degree of degradation of the client-side VR stream capture relative to the source VR stream capture.

Measuring quality-of-experience (QoE) for virtual reality (VR) streaming content is disclosed. A network computing device receives a client-side VR stream capture and a client pose data set that are generated by a client computing device based on a VR content and one or more induced network impairments (e.g., latency, packet loss, and/or jitter, as non-limiting examples). Using the same VR content and the client pose data set, the network computing device generates a source VR stream capture that is not subjected to the one or more induced network impairments. The network computing device performs a frame-by-frame comparison of the client-side VR stream capture and the source VR stream capture. Based on the frame-by-frame comparison, the network computing device generates a QoE metric that indicates a degree of degradation of the client-side VR stream capture relative to the source VR stream capture.

Techniques for modifying data packet transmission characteristics by an intermediate node in a network are disclosed. An intermediate node in a data transmission network determines a current estimated transmission time for packets being transmitted from the source node to the intermediate node. The node analyzes a data packet to determine a Quality of Service (QoS) requirement for transmission of the first data packet. Based on the current estimated transmission time for packets being transmitted from the source node to the intermediate node and the QoS requirement for transmission of the first data packet, the intermediate node selects one or more transmission characteristics for forwarding the first data packet toward the destination node. The intermediate node transmits the packet toward the destination node in accordance with the one or more transmission characteristics.

Methods of managing audio data transmissions over a network disclosed herein may include the step of selecting a client device from a plurality of client devices as a participating device, each client device of the plurality of client devices being in data communication with a network. The methods may include the step of signaling the participating device over said network thereby initiating transmitting of audio data from the participating device at least in part over said network for live broadcasting, the audio data being indicative of a speaking voice being input into a participating device microphone of the participating device. The methods may include the step of minimizing latency in transmitting of the audio data by throttling data being communicated over said network by one or more client devices of the plurality of client devices only while the participating device is transmitting audio data over said network.

Methods of managing audio data transmissions over a network disclosed herein may include the step of selecting a client device from a plurality of client devices as a participating device, each client device of the plurality of client devices being in data communication with a network. The methods may include the step of signaling the participating device over said network thereby initiating transmitting of audio data from the participating device at least in part over said network for live broadcasting, the audio data being indicative of a speaking voice being input into a participating device microphone of the participating device. The methods may include the step of minimizing latency in transmitting of the audio data by throttling data being communicated over said network by one or more client devices of the plurality of client devices only while the participating device is transmitting audio data over said network.

Methods and apparatuses for detecting timestamp discontinuities and video resolution discontinuities within a packet stream and marking locations of the detected discontinuities within the packet stream are described. Prior to transmission of the packet stream, an electronic device may perform timestamp discontinuity detection by acquiring a sequence of packets to be transmitted, identifying a first timestamp associated with an earliest packet within the sequence of packets, identifying a second timestamp associated with a latest packet within the sequence of packets, determining a timestamp time difference between the first timestamp and the second timestamp, determining a maximum chunk time difference based on a data rate at which the sequence of packets were encoded and a data size of the encoded packets, and detecting that a timestamp discontinuity exists within the sequence of packets if the timestamp time difference is greater than the maximum chunk time difference.