all the results. This metric is called mean opinion score (MOS) and it is the basis of most of the objective video quality metrics, which try to model video quality in a way which correlates as much as possible with MOS [2]. These kinds of solutions, however, are normally quite costly in terms of computing power required, and require measuring the video quality in the pixel domain, typically both before and after the degradation. Thus they are widely used in video codec calibration, but very limitedly in network monitoring.Multimedia quality of service is typically characterized by the Media Delivery Index (MDI) [10], which is a de facto standard in IPTV deployments. MDI is composed of two measurements: the packet loss rate (PLR), and the delay factor (DF), a measure of packet jitter. It is quite useful to model network issues and effective packet loss, but it assumes that all

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Abstract-Real-time monitoring of multimedia Quality of Experience is a critical task for the providers of multimedia delivery services: from television broadcasters to IP content delivery networks or IPTV. For such scenarios, meaningful metrics are required which can generate useful information to the service providers that overcome the limitations of pure Quality of Service monitoring probes. However, most of objective multimedia quality estimators, aimed at modeling the Mean Opinion Score, are difficult to apply to massive quality monitoring. Thus we propose a lightweight and scalable monitoring architecture called Qualitative Experience Monitoring (QuEM), based on detecting identifiable impairment events such as the ones reported by the customers of those services. We also carried out a subjective assessment test to validate the approach and calibrate the metrics. Preliminary results of this test set support our approach.

We have developed a parametric model to quantify the Key Quality Indicators which affect video-based Tele-operated Driving (ToD) over a mobile network, as well as their relationship with the network Key Performance Indicators. This model can be easily used to specify Quality of Service policies (e.g. through network slicing) that guarantee the required conditions for remote driving on specific areas. We have used our model to validate the feasibility of deploying remote-assisted driving in different real networks, both from current 4G deployments and from pre-commercial and commercial 5G pilots. Our results show that some ToD services (supervision and, up to some point, parking) may be feasible with high-end existing 5G networks. However, full remote driving requires some improvements in the system, particularly to reduce end-to-end latency, increase uplink performance, and minimize service losses. Both the model and its results will be used in the framework of European Union H2020 project 5G-MOBIX to deploy a ToD proof-of-concept in the cross-border corridor between Spain and Portugal.

Bespoke solutions are adopted to deliver video content for specific display devices depending upon a particular delivery network. This ad‐hoc approach limits the number of viewers that can be reached depending upon their device type and the delivery network implemented. In the video ecosystem, we present a general solution built upon a modular, scalable, flexible, and multi‐vendor video headend architecture suitable for tailoring video content to specific networks, user display screens, and profiles. This video headend solution encompasses content acquisition, processing, digital rights management (DRM), and quality monitoring for live video and video on demand (VoD) with the highest quality, optimized for the target audience. This solution helps to drive down operational costs for service providers while generating new revenues by tapping into all viewer segments and providing a new and richer experience to viewers. © 2012 Alcatel‐Lucent.

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstreambased estimation.