The CASPER user-centric approach for advanced service provisioning in mobile networks

Abstract This paper presents an overview of the project CASPER, 1 a 4-year Marie Curie Research and Innovation Staff Exchange (RISE) project running between 2016 and 2020, describing its objectives, approach, architecture, tools and key achievements. CASPER combines academic and industrial forces towards leveraging the expected benefits of Quality of Experience (QoE) exploitation in future networks. In order to achieve that, a QoE orchestrator has been proposed which implements the basic functionalities of QoE monitoring, estimation and management. With means of simulation and testbed emulation, CASPER has managed to develop a proprietary SDN Controller, which implements QoE-based traffic rerouting for the challenging scenario of HTTP adaptive video streaming, leading to more stable and higher QoE scores compared to a state-of-the-art SDN Controller implementation

Network-aware video streaming for future media Internet

272 p

AVQBits-adaptive video quality model based on bitstream information for various video applications

The paper presents AVQBits, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that AVQBits predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of AVQBits presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference AVQBits model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, AVQBits encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of AVQBits are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four AVQBits variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of AVQBits for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the AVQBits instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module are made publicly available for the community for further research

Database of the Italian disdrometer network

In 2021, a group of seven Italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation, giving birth to the Italian Group of Disdrometry (in Italian named Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/, last access: 16 May 2023). GID has made freely available a database of 1 min records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network was composed of eight laser disdrometers belonging to six different Italian institutions (including research centres, universities, and environmental regional agencies). This work aims to document the technical aspects of the Italian DSD database consisting of 1 min sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same data record length, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens up new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records and beyond. The Version 01 GID database can be downloaded at https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022), while Version 02 can be downloaded at https://doi.org/10.5281/zenodo.7708563 (Adirosi et al., 2023). The difference among the two versions is the diameter–fall velocity relation used for the DSD computation

DASHbed: a testbed framework for large scale empirical evaluation of real-time DASH in wireless scenarios

Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users’ demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P.1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms

Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O tráfego de serviços de vídeo multimídia está crescendo rapidamente nas redes móveis nos últimos anos. Os serviços de vídeo que usam técnicas de Dynamic Adaptive Streaming sobre HTTP (DASH) dominaram o tráfego total da Internet para transportar o tráfego de vídeo. Espera-se que as operadoras de rede móvel (Mobile Network Operators - MNOs) continuem atendendo a essa demanda crescente por tráfego de vídeo suportado por DASH, ao mesmo tempo em que fornecem uma alta qualidade de experiência (Quality of Experience - QoE) aos usuários finais. Além disso, as operadoras precisam ter um conhecimento claro acerca da qualidade de vídeo percebida pelos usuários finais e relacioná-la com o monitoramento em nível de rede, ou com informações de telemetria para identificação de problemas, análise da causa raiz e predição de padrões. Para garantir um gerenciamento de tráfego de rede com reconhecimento de QoE, um pré-requisito é que os MNOs monitorem o tráfego de rede passivamente e realizem medições efetivas de indicadores-chave de desempenho (Key Performance Indicators - KPIs) de QoE, como resoluções, eventos de paralisação, entre outros, que influenciam diretamente a percepção do usuário final. Muitas abordagens da literatura foram propostas para medir os KPIs com o objetivo de fornecer uma qualidade de serviço de vídeo aceitável. A maioria das soluções exige consciência de contexto do usuário final, o que não é viável do ponto de vista do MNO. No entanto, Deep Packet Inspection (DPI), outra solução mais amplamente usada para estimar os KPIs diretamente do tráfego de rede, não é mais uma solução conveniente para as operadoras devido à adoção de criptografia de streaming de vídeo fim-a-fim sobre TCP (HTTPs) e QUIC. Portanto, o aprendizado de máquina (Machine Learning - ML) passou a ser recentemente aceito como uma solução bem reconhecida para estimar KPIs de QoE, analisando os padrões de tráfego criptografados bem como estatísticas como qualidade de serviço (Quality of Service - QoS). Este trabalho apresenta uma abordagem mais refinada e leve, baseada em aprendizado de máquina, denominada Edge QoE Probe, para estimar QoE do usuário final para o serviço de vídeo DASH, monitorando passivamente o tráfego de rede criptografado na borda da rede. Nossa abordagem pode avaliar vários KPIs de QoE, como por exemplo resolução, taxa de bits, proporção de paralisação, entre outros, tanto em tempo real quanto por sessão. Além disso, neste trabalho investigamos o desempenho do vídeo DASH sobre o protocolo de transporte tradicional TCP (HTTPs) e QUIC. Para este propósito, avaliamos experimentalmente diferentes traces de rede celular em um ambiente emulado de alta fidelidade e comparamos o desempenho comportamental de algoritmos Adaptive Bitrate Streaming (ABS) considerando KPIs de QoE sobre TCP (HTTPs) e QUIC. Nossos resultados empíricos mostram que os algoritmos tradicionais de ABS usando QUIC como transporte precisariam alterações específicas para melhorar o desempenho em termos de QoE de vídeo baseados em DASHAbstract: Multimedia video services traffic is rapidly growing in mobile networks in recent years. Video services using Dynamic Adaptive Streaming over HTTP (DASH) techniques have dominated the total internet traffic to carry video traffic. Mobile Network Operators (MNOs) are expected to run on with this growing demand for DASH-supported video traffic while providing a high Quality of Experience (QoE) to the end-users. Besides, operators need to have a crystal notion of video quality perceived by the end-users and correlate them with network-level monitoring or telemetry information for problem identification, root cause analysis, and pattern prediction. To ensure QoE–aware network traffic management, a prerequisite for the MNOs is to monitor the network traffic passively and measure objective QoE Key Performance Indicators (KPIs) (such as resolutions and stalling events) effectively that directly influence end-user subjective feedback. Many literature approaches have been proposed to measure the KPIs aimed to deliver acceptable video service quality. Most of the solutions require end-user awareness, which is not viable from the MNOs' perspective. However, Deep Packet Inspection (DPI), another most widely used solution to estimate the KPIs directly from network traffic, is not a convenient solution anymore for the operators due to the adoption of end-to-end video streaming encryption over TCP (HTTPs) and QUIC transport protocol. Hence, in recent, Machine Learning (ML) has been accepted as a well-recognized solution for estimating QoE KPIs by analyzing the encrypted traffic patterns and statistics as Quality of Service (QoS). This work presents an ML-based lightweight and fine-grained Edge QoE Probe approach to estimate the end-user QoE for DASH video service by passively monitoring the encrypted network traffic on the edge of the network. Our approach can assess numerous QoE KPIs (such as resolution, bit-rate, quality switches, startup delay, and stall ratio) both in a real-time and per-session manner. Moreover, we investigate the DASH video service performance over the traditional TCP (HTTPs) and QUIC transport protocol in this work. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the behavioral performance of Adaptive Bitrate Streaming (ABS) algorithms considering QoE KPIs over TCP (HTTPs) and QUIC. Our empirical results show that QUIC suffers from traditional state-of-the-art ABS algorithms' ineffectiveness to improve video streaming performance without specific changesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaFuncam

Edge cloud architectures : a survey

Nowadays the number of connected devices is growing sharply. Mobile phones and other IoT devices are inherent parts of everyday life and used everywhere. The amount of data generated by IoT devices and mobile phones is enormous, which causes network congestions. In turn, the usage of centralized cloud architecture increases delay and cause jitter. To address those issues the research community discussed the new trend of decentralization – edge computing. There are different edge compute architectures suggested by various researchers. Some are more popular and supported by global companies. Most of those architectures have similarities. In this research, we reviewed seven edge compute architectures. This thesis is a comparative analysis carried out by using key attributes and presentation of the Venn diagram to select the right edge compute architecture