DETERMINING LOSS OF IP PACKETS
    4.
    发明授权
    DETERMINING LOSS OF IP PACKETS 有权
    BESTIMMUNG DES VERLUSTS VON IP-PAKETEN

    公开(公告)号:EP2622819B1

    公开(公告)日:2017-03-08

    申请号:EP11829680.5

    申请日:2011-09-14

    IPC分类号: H04L29/06

    摘要: 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 (&Dgr;cc) between the sequence numbers, and v) determining (506) a number (k) of lost IP packets, as a function of the difference (&Dgr;cc) between the sequence numbers. Related computer readable medium and customer equipment are also described.

    摘要翻译: 一种确定通过网络(102)传送的IP分组的丢失的方法,其中每个IP分组包括多个数据分组。 该方法包括:i)从第一IP分组(41)提取(501)第一数据分组,ii)从第二IP分组提取(502)第二数据分组,iii)检索(503,504)第一序列 来自第一数据分组的数量和来自第二数据分组的第二序列号,其中每个序列号表示数据分组的序列,并且具有属于一组唯一值的值,iv)计算( 505)序列号之间的差异(&Dgr; cc),以及v)确定(506)丢失IP分组的数量(k),作为序列号之间的差异(&Dgr; cc)的函数。 还描述了相关的计算机可读介质和客户设备。

    PREDICTING MULTIMEDIA SESSION MOS
    6.
    发明公开

    公开(公告)号:EP3420698A1

    公开(公告)日:2019-01-02

    申请号:EP17708715.2

    申请日:2017-02-22

    摘要: 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.