The rise in the consumption of multimedia content has resulted in the demand to provide an exceptional user experience. However, modelling user-perceived Quality of Experience (QoE) presents a complex challenge. This fuels ongoing efforts to understand and measure QoE. To assess it, researchers rely on both subjective quality assessment (where users view and rate videos) and objective quality assessment (where quality metrics are designed to calculate perceived video quality). Although subjective evaluation is essential for mapping user experience to objective metrics, many studies omit the analysis of the impact of content preferences on user experience. This oversight limits our understanding of how video content influences QoE perception.To address this limitation, we created a multi-content video dataset with impairments based on realistic network conditions. Furthermore, we conducted a subjective study in a controlled environment evaluating the impact of user content category preferences and user video preference on QoE. One of our initial findings is that the actual video denoted as uninteresting by users had the most negative impact on the overall user QoE, but had no impact on user perception of other video degradations.

Accurate Throughput Prediction (TP) represents a real challenge for reliable adaptive streaming in challenging mediums, such as cellular networks. State-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy for various multimedia systems. This paper illustrates that designing blackbox TP engines that depend solely on the model’s capacity and power of learning does not achieve consistent accuracy across all throughput ranges. Additionally, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy. MATURE’s prediction involves characterising the operating context before estimating the network throughput. We show that MATURE delivers consistent, accurate prediction for all throughput ranges in both 4G and 5G networks. We also show that light-weight mature models that use quantized parameters maintain their accuracy while featuring up to 100x faster inference, thus making them suitable for mobile implementation. Our real video streaming experiments further show that MATURE improves the average user Quality of Experience (QoE) by up to 20% when compared to other throughput prediction methods.

Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, which relies on efficient state estimation, represents a challenging computational task and requires efficient simulation tools for power system steady-state analyses. Motivated by this observation, we propose JuliaGrid, an open-source framework written in the Julia programming language, designed for high performance execution across multiple platforms. The framework implements observability analysis, weighted least-squares and least-absolute value estimators, bad data analysis, and various algorithms related to phasor measurements. To complete power system analysis, the framework includes power flow and optimal power flow, enabling measurement generation for the state estimation routines. Leveraging computationally efficient algorithms, JuliaGrid solves large-scale systems across all methods, offering competitive performance compared to other open-source tools. It is specifically designed for quasi-steady-state analysis, with automatic detection and reuse of computed data to boost performance. These capabilities are validated on systems with 10000, 20000 and 70000 buses.

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4% - 90% in critical network conditions when compared to state-of-the-art.

The continuous rise of multimedia entertainment has led to an increased demand for delivering outstanding user experience of multimedia content. However, modeling user-perceived Quality of Experience (QoE) is a challenging task, resulting in efforts for better understanding and measurement of user-perceived QoE. To evaluate user QoE, subjective quality assessment, where people watch and grade videos, and objective quality assessment in which videos are graded using one or many objective metrics are conducted. While there is a plethora of video databases available for subjective and objective video quality assessment, these videos are artificially infused with various temporal and spatial impairments. Videos being assessed are artificially distorted with startup delay, bitrate changes, and stalls due to rebuffering events. To conduct a more credible quality assessment, a reproduction of original user experiences while watching different types of streams on different types and quality of networks is needed. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open source framework for re-creating adaptively streamed video in real networks. The framework takes inputs in the form of video logs captured by the client in a non-regulated setting, along with an .mpd file or a YouTube URL. The ultimate result is a video sequence that encompasses all the data extracted from the video log. DashReStreamer also calculates popular video quality metrics like PSNR, SSIM, MS-SSIM, and VMAF. Finally, DashReStreamer allows creating impaired video sequences from the popular streaming platform YouTube. As a demonstration of framework usage, we created a database of 332 realistic video clips, based on video logs collected from real mobile and wireless networks. Every video clip is supplemented with bandwidth trace and video logs used in its creation and also with objective metrics calculation reports. In addition to dataset, we performed subjective evaluation of video content, assessing its effect on overall user QoE. We believe that this dataset and framework will allow the research community to better understand the impacts of video QoE dynamics.

AI-driven data analysis methods have garnered attention in enhancing the performance of wireless networks. One such application is the prediction of downlink throughput in mobile cellular networks. Accurate throughput predictions have demonstrated significant application benefits, such as improving the quality of experience in adaptive video streaming. However, the high degree of variability in cellular link behaviour, coupled with device mobility and diverse traffic demands, presents a complex problem. Numerous published studies have explored the application of machine learning to address this problem, displaying potential when trained and evaluated with traffic traces collected from operational networks. The focus of this paper is an empirical investigation of machine learning-based throughput prediction that runs in real-time on a smartphone, and its evaluation with video streaming in a range of real-world cellular network settings. We report on a number of key challenges that arise when performing prediction “in the wild”, dealing with practical issues one encounters with online data (not traces) and the limitations of real smartphones. These include data sampling, distribution shift, and data labelling. We describe our current solutions to these issues and quantify their efficacy, drawing lessons that we believe will be valuable to network practitioners planning to use such methodologies in operational cellular networks.

Different industries are observing the positive impact of 360 video on the user experience. However, the performance of VR systems continues to fall short of customer expectations. Therefore, more research into various design elements for VR streaming systems is required. This study introduces a SW tool that offers straight-forward encoding platforms to simplify the encoding of DASH VR videos. In addition, we developed a dataset composed of 9 VR videos encoded with seven tiling configurations, four segment durations, and up to four different bitrates. A corresponding tile size dataset is also provided, which can be utilised to power network simulations or trace-driven emulations. We analysed the traffic load of various films and encoding setups using the dataset that was presented. Our research indicates that, while smaller tile sizes reduce traffic load, video decoding may require more computational power.

Accurate prediction of cellular link performance represents a corner stone for many adaptive applications, such as video streaming. State-of-the-art solutions focus on distributed device-based methods relying on historic throughput and PHY metrics obtained through device APIs. In this paper, we study the impact of centralised solutions that integrate information collected from other network nodes. Specifically, we develop and compare machine learning inference engines for both distributed and centralised approaches to predict the LTE physical resource blocks using ns3-simulation. Our results illustrate that network load represents the most important feature in the centralised approaches resulting in halving the RB prediction error to 14% in comparison to 28 % for the distributed case.

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.