Telefonaktiebolaget Lm Ericsson (publ)

Tomas Lundberg

Gunnar Heikkilä

Jing Fu

Junaid Shaikh

Tor Björn Minde

Stefan Bruhn

Jonas Svedberg

David Lindegren

Stefan Håkansson

Selim Ickin

Jörgen Gustafsson

David Lindero

Stefan Hakansson

Arne Steinarson

An initial bitrate is selected for a video delivery session by a user equipment () performing, during a time window from initiation of a video player application in the user equipment () up to selection of a video content, network measurements of a current condition of a network used to deliver the video content to the user equipment (). A respective initial buffer duration is provided for each bitrate available for the video content and where these respective initial buffer durations are predicted based on at least one network metric derived from the network measurements. An initial bitrate for delivering the video content over the network to the user equipment is selected based on the respective initial buffer durations.

Initial bitrate selection for a video delivery session

A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Method and scoring node for estimating a user's quality of experience for a delivered service

Mechanisms for predicting a multimedia session MOS are provided. The multimedia session has a video session and an audio session. The video quality and the audio quality are represented by vectors of per-time-unit scores of video quality and audio quality, respectively. The multimedia session is represented by a vector of the rebuffering start times of each rebuffering event, and a vector of the rebuffering durations of each rebuffering event. Audiovisual quality features are generated from the vectors of per-time-unit scores of video and audio quality. Buffering features are generated from the vector of rebuffering start times of each rebuffering event and the vector of rebuffering durations of each rebuffering event. A multimedia session MOS is then estimated based on the generated audiovisual quality features and the generated buffering features.

Quality estimation of adaptive multimedia streaming

It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.

Predicting multimedia session MOS

There are provided mechanisms for predicting a multimedia session MOS. The multimedia session comprises a video session and an audio session, wherein video quality is represented by a vector of per-time-unit scores of video quality and wherein audio quality is represented by is a vector of per-time-unit scores of audio quality. The multimedia session is represented by a vector of rebuffering start times of each rebuffering event, a vector of rebuffering durations of each rebuffering event, and an initial buffering duration being the time between an initiation of the multimedia session and a start time of the multimedia session. The method comprises generating audiovisual quality features from the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality. The audiovisual quality features comprise: a vector of per-time-unit scores of audiovisual quality, calculated as a polynomial function of the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality; a weighted combination of the per-time-unit scores of audiovisual quality, wherein the weights are exponential functions of a time since the start time of multimedia session and a multimedia session duration; a negative bias representing how a sudden drop in per-time-unit scores of audiovisual quality affects the multimedia session MOS; and a term representing a degradation due to oscillations in the per-time-unit-scores of audiovisual quality. The method comprises generating buffering features from the vector of rebuffering start times of each rebuffering event, calculated from the start time of multimedia session, and the vector of rebuffering durations of each rebuffering event. The method comprises estimating a multimedia session MOS from the generated audiovisual quality features and the generated buffering features.

A method of determining loss of IP packets that are transported over a network (), where each IP packet comprises a number of data packets. The method comprises: i) extracting () a first data packet from a first IP packet (), ii) extracting () a second data packet from a second IP packet, iii) retrieving () a first sequence number from a first data packet and a second sequence number from a second data packet, where each of the sequence numbers is indicative of a sequence of data packets and has a value belonging to a set of a number of unique values, iv) calculating () a difference (&#x394;) between the sequence numbers, and v) determining () a number (k) of lost IP packets, as a function of the difference (&#x394;) between the sequence numbers. Related computer readable medium and customer equipment are also described.

Determining loss of IP packets

A method and arrangement for dynamically adapting thresholds used for selecting a codec mode to be used is presented. Thresholds are adapted in response to the current received signal quality. An estimate of actual prevailing received signal quality is obtained on which the adaptation is based. The present invention can be applied either on the mobile terminal side or on the network side, working on the uplink and/or the downlink. The thresholds can be modified on the receiving side, or, when operating in the network and working on the downlink, the threshold adaptation can be initiated in the terminal.

Methods and arrangements for adaptive thresholds in codec selection

A multi-channel linear predictive analysis-by-synthesis signal encoding method detects (S, S) inter-channel correlation and select one of several possible encoding modes (SSS) based on the detected correlation.

Multi-channel signal encoding and decoding

A multi-channel linear predictive analysis-by-synthesis signal encoding method determines (S) a leading channel and encodes the leading channel as an embedded bitstream. Thereafter trailing channels are encoded as a discardable bitstream exploiting cross-correlation to the leading channel.

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Luleå, Sweden

Tomas Lundberg