Mixed streaming of video over wireless networks

In recent years, transmission of video over the Internet has become an important application. As wireless networks are becoming increasingly popular, it is expected that video will be an important application over wireless networks as well. Unlike wired networks, wireless networks have high data loss rates. Streaming video in the presence of high data loss can be a challenge because it results in errors in the video.Video applications produce large amounts of data that need to be compressed for efficient storage and transmission. Video encoders compress data into dependent frames and independent frames. During transmission, the compressed video may lose some data. Depending on where the packet loss occurs in the video, the error can propagate for a long time. If the error occurs on a reference frame at the beginning of the video, all the frames that depend on the reference frame will not be decoded successfully. This thesis presents the concept of mixed streaming, which reduces the impact of video propagation errors in error prone networks. Mixed streaming delivers a video file using two levels of reliability; reliable and unreliable. This allows sensitive parts of the video to be delivered reliably while less sensitive areas of the video are transmitted unreliably. Experiments are conducted that study the behavior of mixed streaming over error prone wireless networks. Results show that mixed streaming makes it possible to reduce the impact of errors by making sure that errors on reference frames are corrected. Correcting errors on reference frames limits the time for which errors can propagate, thereby improving the video quality. Results also show that the delay cost associated with the mixed streaming approach is reasonable for fairly high packet loss rates

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor

On the Performance of Adaptive Bitrate Streaming and Parallel Cloud Applications

As shown in recent studies, video streaming is by far the biggest category of backbone Internet traffic in the US. As a measure to reduce the cost of highly over-provisioned physical infrastructures while remaining the quality of video services, many streaming service providers started to use cloud services, where physical resources can be dynamically allocated based on current demand. In this dissertation, we seek to evaluate and improve the performance for both Adaptive Bitrate (ABR) video streaming and cloud applications. First, we present a set of measurement studies for ABR streaming applications. Using the data from the application, network, and physical layers in different network environments, we identify the key factors that can impact the quality of video delivering services. Then we develop and evaluate a set of new ABR streaming quality adaptation algorithms to improve the user playback experience. In addition, we explore the options for better energy efficiency of ABR video transcoding services and parallel cloud applications. We define a set of energy management policies to enable the utilization of renewable energy sources. We show that, for both applications, by effectively utilizing the renewable energy, our policies can significantly reduce the grid energy usage and corresponding energy cost, while ensuring a satisfying application performance

HbbTV-compliant Platform for Hybrid Media Delivery and Synchronization on Single- and Multi-Device Scenarios

[EN] The combination of broadcast and broadband (hybrid) technologies for delivering TV related media contents can bring fascinating opportunities. It is motivated by the large amount and diversity of media contents, together with the ubiquity and multiple connectivity capabilities of modern consumption devices. This paper presents an end-to-end platform for the preparation, delivery, and synchronized consumption of related hybrid (broadcast/broadband) media contents on a single device and/or on multiple close-by devices (i.e., a multi-device scenario). It is compatible with the latest version of the Hybrid Broadcast Broadband TV (HbbTV) standard (version 2.0.1). Additionally, it provides adaptive and efficient solutions for key issues not specified in that standard, but that are necessary to successfully deploy hybrid and multidevice media services. Moreover, apart from MPEG-DASH and HTML5, which are the broadband technologies adopted by HbbTV, the platform also provides support for using HTTP Live Streaming and Real-time Transport Protocol and its companion RTP Control Protocol broadband technologies. The presented platform can provide support for many hybrid media services. In this paper, in order to evaluate it, the use case of multi-device and multi-view TV service has been selected. The results of both objective and subjective assessments have been very satisfactory, in terms of performance (stability, smooth playout, delays, and sync accuracy), usability of the platform, usefulness of its functionalities, and the awaken interest in these kinds of platforms.This work was supported in part by the "Fondo Europeo de Desarrollo Regional" and in part by the Spanish Ministry of Economy and Competitiveness through R&D&I Support Program under Grant TEC2013-45492-R.Boronat, F.; Marfil-Reguero, D.; Montagud, M.; Pastor Castillo, FJ. (2017). HbbTV-compliant Platform for Hybrid Media Delivery and Synchronization on Single- and Multi-Device Scenarios. IEEE Transactions on Broadcasting. 1-26. https://doi.org/10.1109/TBC.2017.2781124S12

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor

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

Robust Mobile Data Transport: Modeling, Measurements, and Implementation

Advances in wireless technologies and the pervasive influence of multi-homed devices have significantly changed the way people use the Internet. These changes of user behavior and the evolution of multi-homing technologies have brought a huge impact to today\u27s network study and provided new opportunities to improve mobile data transport. In this thesis, we investigate challenges related to human mobility, with emphases on network performance at both system level and user level. More specifically, we seek to answer the following two questions: 1) How to model user mobility in the networks and use the model for network provisioning? 2) Is it possible to utilize network diversity to provide robust data transport in wireless environments? We first study user mobility in a large scale wireless network. We propose a mixed queueing model of mobility and show that this model can accurately predict both system-level and user-level performance metrics. Furthermore, we demonstrate how this model can be used for network dimensioning. Secondly, with the increasing demand of multi-homed devices that interact with heterogeneous networks such as WiFi and cellular 3G/4G, we explore how to leverage this path diversity to assist data transport. We investigate the technique of multi-path TCP (MPTCP) and evaluate how MPTCP performs in the wild through extensive measurements in various wireless environments using WiFi and cellular 3G/4G simultaneously. We study the download latencies of MPTCP when using different congestion controllers and number of paths under various traffic loads and over different cellular carriers. We further study the impact of short flows on MPTCP by modeling MPTCP\u27s delay startup mechanism of additional flows. As flow sizes increase, we observe that traffic in cellular networks exhibits large and varying packet round trip times, called bufferbloat. We analyze the phenomenon of bufferbloat, and illustrate how bufferbloat can result in numerous MPTCP performance issues. We provide an effective solution to mitigate the performance degradation. Finally, as popular content is replicated at multiple locations, we develop mechanisms that take advantage of this source diversity along with path diversity to provide robust mobile data transport. We demonstrate this in the context of online video streaming, because of its popularity and significant contribution to Internet traffic. We therefore propose MSPlayer, a client-based solution for online video streaming that adjusts network traffic distribution over each path to network dynamics. MSPlayer bypasses the deployment limitations of MPTCP while maintaining the benefits of path diversity, and exploits different content sources simultaneously. MSPlayer can significantly reduce video start-up latency and quickly refill playout buffer for high quality video streaming. We evaluate MSPlayer\u27s performance through YouTube

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness