YouTube Live is one of the most popular services on the Internet, enabling an easy streaming of a live video with acceptable video quality. Thus, understanding user´s perception of this service is of the utmost importance for network operators. As in other videostreaming services, YouTube Live traffic is sometimes affected by delays due to unfavourable network conditions, which translate into unacceptable initial reproduction times or image freezes as a result of client´s buffer underrun. Detecting these events is key to ensure an adequate Quality of experience (QoE). Unfortunately, data encryption makes it very difficult for operators to monitor QoE from packet-level data collected in network interfaces. In this paper, an analytical model to estimate the QoE for encrypted YouTube Live service from packet-level data collected in the interfaces of a wireless network is presented. The inputs to the model are Transport Control Protocol (TCP)/Internet Protocol (IP) metrics, from which three Service Key Performance Indicators (S-KPIs) are estimated, namely initial video play start time, video interruption duration and video interruption. The model is developed with an experimental platform, consisting of a user terminal agent, a WiFi wireless network, a network-level emulator and a probe software. Model assessment is carried out by comparing S-KPI estimates with measurements from the terminal agent under different network conditions introduced by the network emulator.