Cloud-gaming:Analysis of Google Stadia traffic

Interactive, real-time, and high-quality cloud video games pose a serious challenge to the Internet due to simultaneous high-throughput and low round trip delay requirements. In this paper, we investigate the traffic characteristics of Stadia, the cloud-gaming solution from Google, which is likely to become one of the dominant players in the gaming sector. To do that, we design several experiments, and perform an extensive traffic measurement campaign to obtain all required data. Our first goal is to gather a deep understanding of Stadia traffic characteristics by identifying the different protocols involved for both signalling and video/audio contents, the traffic generation patterns, and the packet size and inter-packet time probability distributions. Then, our second goal is to understand how different Stadia games and configurations, such as the video codec and the video resolution selected, impact on the characteristics of the generated traffic. Finally, we aim to evaluate the ability of Stadia to adapt to different link capacity conditions, including those cases where the capacity drops suddenly. Our results and findings, besides illustrating the characteristics of Stadia traffic, are also valuable for planning and dimensioning future networks, as well as for designing new resource management strategies

Estimating WebRTC Video QoE Metrics Without Using Application Headers

The increased use of video conferencing applications (VCAs) has made it critical to understand and support end-user quality of experience (QoE) by all stakeholders in the VCA ecosystem, especially network operators, who typically do not have direct access to client software. Existing VCA QoE estimation methods use passive measurements of application-level Real-time Transport Protocol (RTP) headers. However, a network operator does not always have access to RTP headers in all cases, particularly when VCAs use custom RTP protocols (e.g., Zoom) or due to system constraints (e.g., legacy measurement systems). Given this challenge, this paper considers the use of more standard features in the network traffic, namely, IP and UDP headers, to provide per-second estimates of key VCA QoE metrics such as frames rate and video resolution. We develop a method that uses machine learning with a combination of flow statistics (e.g., throughput) and features derived based on the mechanisms used by the VCAs to fragment video frames into packets. We evaluate our method for three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected from both (1), controlled in-lab network conditions, and (2) real-world networks from 15 households. We show that the ML-based approach yields similar accuracy compared to the RTP-based methods, despite using only IP/UDP data. For instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second intervals in the real-world data, which is only 1.76% lower than using the application-level RTP headers.Comment: 20 page

Congestion Control using FEC for Conversational Multimedia Communication

In this paper, we propose a new rate control algorithm for conversational multimedia flows. In our approach, along with Real-time Transport Protocol (RTP) media packets, we propose sending redundant packets to probe for available bandwidth. These redundant packets are Forward Error Correction (FEC) encoded RTP packets. A straightforward interpretation is that if no losses occur, the sender can increase the sending rate to include the FEC bit rate, and in the case of losses due to congestion the redundant packets help in recovering the lost packets. We also show that by varying the FEC bit rate, the sender is able to conservatively or aggressively probe for available bandwidth. We evaluate our FEC-based Rate Adaptation (FBRA) algorithm in a network simulator and in the real-world and compare it to other congestion control algorithms

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe

With the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data-sets public to the interested experimenter. Building such data-sets is very welcomed with the research community, opening doors to applying data science to network data-sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis. We exploit data collection from MONROE to apply data science over network data-sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem. Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees) and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and CIAN can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7. In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren

GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines

Machine learning for Quality of Experience in real-time applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

IMPLEMENTASI ARSITEKTUR REST DALAM APLIKASI VIDEO CONFERENCE DENGAN FITUR PENGENALAN EMOSI MENGGUNAKAN WEBRTC

Emosi memiliki pengaruh yang signifikan dalam proses pembelajaran, karena dapat memengaruhi ingatan dan tindakan. Saat ini, berbagai jenis Application Programming Interface (API) pengenalan emosi telah tersedia, memberikan kesempatan untuk menerapkan aplikasi pengenalan emosi dan aplikasi visualisasi secara real-time. Mendapatkan data real-time biasanya dilakukan melalui penggunaan suatu API, seperti Representational State Transfer (REST). Terdapat beberapa aplikasi yang digunakan untuk mengenali emosi siswa dalam pembelajaran daring, salah satunya adalah aplikasi video conference. Salah satu teknologi yang digunakan dalam membangun suatu aplikasi video conference yaitu WebRTC. Penelitian ini bertujuan untuk mengembangkan aplikasi WebRTC pengenalan emosi dengan arsitektur REST, serta menganalisis performa aplikasi back-end dan front-end. Performa aplikasi back-end diukur menggunakan metrik Quality of Service (QoS) seperti response time, throughput, memory utilization, dan CPU Load. Performa aplikasi front-end diukur dengan metrik Google Lighthouse Performance. Hasilnya pada aplikasi back-end pada endpoint Recognition Grup nilai rata rata Response Time sebesar 2567,34 ms, Throughput 36,89 request/s, Memory Utilization 622,05 MB, CPU Load 9,53 %. Sedangkan pada endpoint Recognition Individu nilai rata rata Response Time sebesar 3209,18 ms, Throughput 29,39 request/s, Memory Utilization 623,96 MB, CPU Load 7,67 %. Hasilnya pada aplikasi front-end nilai rata - rata pada metrik FCP sebesar 936,1 ms, SI sebesar 1095,28 ms, LCP sebesar 1154,54 ms, TTI sebesar 972,55ms, TBT sebesar 0,728 dan CLS sebesar 0. Dengan demikian keseluruhan metrik menghasilkan nilai 96% Performance Score sehingga performa aplikasi front-end dapat dikatakan Baik. -------- Emotions have a significant impact on the learning process as they can influence memory and actions. Currently, various types of Emotion Recognition Application Programming Interfaces (APIs) are available, providing opportunities to implement real-time emotion recognition and visualization applications. Real-time data acquisition is typically accomplished through the use of an API, such as Representational State Transfer (REST). One of the applications used for recognizing students' emotions in online learning is video conferencing applications. WebRTC is one of the technologies used to build a video conferencing application. This research aims to develop a WebRTC-based emotion recognition application with a REST architecture and analyze the performance of the backend and front-end components. The back-end application's performance is measured using Quality of Service (QoS) metrics, including response time, throughput, memory utilization, and CPU load. The front-end application's performance is measured using Google Lighthouse Performance metrics. The results for the back-end application show that, in the Recognition Group endpoint, the average response time is 2567.34 ms, throughput is 36.89 requests/s, memory utilization is 622.05 MB, and CPU load is 9.53%. Meanwhile, in the Recognition Individual endpoint, the average response time is 3209.18 ms, throughput is 29.39 requests/s, memory utilization is 623.96 MB, and CPU load is 7.67%. The results for the front-end application indicate that the average values for the FCP, SI, LCP, TTI, TBT, and CLS metrics are 936.1 ms, 1095.28 ms, 1154.54 ms, 972.55 ms, 0.728, and 0, respectively. Thus, the overall metrics yield a 96% Performance Score, indicating that the front-end application's performance can be considered Good

Predicting the effect of home Wi-Fi quality on QoE

International audiencePoor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short