Abstract: An initial bitrate is selected for a video delivery session by a user equipment (5, 200) performing, during a time window from initiation of a video player application in the user equipment (5, 200) 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 (5, 200). 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.

Abstract: 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.

Abstract: 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.

Abstract: Apparatus for dispensing a beverage in which a bottle containing the beverage is placed in a cradle and a portion of the beverage flows into a heat exchanger in which the beverage is heated or cooled to a desired temperature. The heat exchanger may be divided into at least two zones with the beverage being heated or cooled to the desired temperature in an inlet zone and the beverage being maintained at the desired temperature in an outlet zone.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An initial bitrate is selected for a video delivery session by a user equipment (5, 200) performing, during a time window from initiation of a video player application in the user equipment (5, 200) 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 (5, 200). 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Apparatus for and method of rapidly chilling a beverage in which a vessel having high thermal mass relative to the amount of beverage to be introduced into the vessel is cooled through contact with a cooling module to a temperature low enough that a volume of beverage introduced into the vessel is rapidly cooled.

Abstract: Apparatus for and method of rapidly chilling a beverage in which a vessel having high thermal mass relative to the amount of beverage to be introduced into the vessel is cooled through contact with a cooling module to a temperature low enough that a volume of beverage introduced into the vessel is rapidly cooled.

Abstract: A method of determining loss of IP packets that are transported over a network (102), where each IP packet comprises a number of data packets. The method comprises: i) extracting (501) a first data packet from a first IP packet (41), ii) extracting (502) a second data packet from a second IP packet, iii) retrieving (503, 504) 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 (505) a difference (?cc) between the sequence numbers, and v) determining (506) a number (k) of lost IP packets, as a function of the difference (?cc) between the sequence numbers. Related computer readable medium and customer equipment are also described.

Abstract: A method of determining loss of IP packets that are transported over a network (102), where each IP packet comprises a number of data packets. The method comprises: i) extracting (501) a first data packet from a first IP packet (41), ii) extracting (502) a second data packet from a second IP packet, iii) retrieving (503, 504) 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 (505) a difference (?cc) between the sequence numbers, and v) determining (506) a number (k) of lost IP packets, as a function of the difference (?cc) between the sequence numbers. Related computer readable medium and customer equipment are also described.