874 research outputs found
Using packet trimming at the edge for in-network video quality adaption
This paper describes the effects of running in-network quality adaption by trimming the packets of layered video streams at the edge. The video stream is transmitted using the BPP transport protocol, which is like UDP, but has been designed to be both amenable to trimming and to provide low-latency and high reliability. The traffic adaption uses the Packet Wash process of Big Packet Protocol (BPP) on the transmitted Scalable Video Coding (SVC) video streams as they pass through a network function which is BPP-aware and embedded at the edge. Our previous work has either demonstrated the use of Software Defined Networking (SDN) controllers to implement Packet Wash directly, or the use of a network function in the core of the network to do the same task. This paper presents our effort to deploy and evaluate such a process at the edge, highlighting the packet trimming algorithm and showing the packet trimming effects on the streams. We compare the performance of transmitting video using BPP and the Packet Wash trimming, against alternative transmission schemes, namely UDP and HTTP adaptive streaming (HAS), presenting a number of quality parameters. The results demonstrate that providing traffic engineering using in-network quality adaption using packet trimming, provides high quality at the receiver
Perceptual video quality assessment: the journey continues!
Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2Â decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future
DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry
Active Queue Management (AQM) is a mechanism employed to alleviate transient
congestion in network device buffers, such as routers and switches. Traditional
AQM algorithms use fixed thresholds, like target delay or queue occupancy, to
compute random packet drop probabilities. A very small target delay can
increase packet losses and reduce link utilization, while a large target delay
may increase queueing delays while lowering drop probability. Due to dynamic
network traffic characteristics, where traffic fluctuations can lead to
significant queue variations, maintaining a fixed threshold AQM may not suit
all applications. Consequently, we explore the question: \textit{What is the
ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED
(Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise
network feedback from In-band Network Telemetry (INT) to feed a Deep
Reinforcement Learning (DRL) model. This model dynamically adjusts the target
delay based on rewards that maximize application Quality of Service (QoS). We
evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH
service. Our findings demonstrate up to a 90x reduction in video stall and a
42x increase in high-resolution video playback quality when the target delay is
adjusted dynamically by DESiRED.Comment: Preprint (Computer Networks under review
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
Colour technologies for content production and distribution of broadcast content
The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model
Situating Data: Inquiries in Algorithmic Culture
Taking up the challenges of the datafication of culture, as well as of the scholarship of cultural inquiry itself, this collection contributes to the critical debate about data and algorithms. How can we understand the quality and significance of current socio-technical transformations that result from datafication and algorithmization? How can we explore the changing conditions and contours for living within such new and changing frameworks? How can, or should we, think and act within, but also in response to these conditions? This collection brings together various perspectives on the datafication and algorithmization of culture from debates and disciplines within the field of cultural inquiry, specifically (new) media studies, game studies, urban studies, screen studies, and gender and postcolonial studies. It proposes conceptual and methodological directions for exploring where, when, and how data and algorithms (re)shape cultural practices, create (in)justice, and (co)produce knowledge
NetShaper: A Differentially Private Network Side-Channel Mitigation System
The widespread adoption of encryption in network protocols has significantly
improved the overall security of many Internet applications. However, these
protocols cannot prevent network side-channel leaks -- leaks of sensitive
information through the sizes and timing of network packets. We present
NetShaper, a system that mitigates such leaks based on the principle of traffic
shaping. NetShaper's traffic shaping provides differential privacy guarantees
while adapting to the prevailing workload and congestion condition, and allows
configuring a tradeoff between privacy guarantees, bandwidth and latency
overheads. Furthermore, NetShaper provides a modular and portable tunnel
endpoint design that can support diverse applications. We present a
middlebox-based implementation of NetShaper and demonstrate its applicability
in a video streaming and a web service application
Mulsemedia Communication Research Challenges for Metaverse in 6G Wireless Systems
Although humans have five basic senses, sight, hearing, touch, smell, and
taste, most multimedia systems in current systems only capture two of them,
namely, sight and hearing. With the development of the metaverse and related
technologies, there is a growing need for a more immersive media format that
leverages all human senses. Multisensory media(Mulsemedia) that can stimulate
multiple senses will play a critical role in the near future. This paper
provides an overview of the history, background, use cases, existing research,
devices, and standards of mulsemedia. Emerging mulsemedia technologies such as
Extended Reality (XR) and Holographic-Type Communication (HTC) are introduced.
Additionally, the challenges in mulsemedia research from the perspective of
wireless communication and networking are discussed. The potential of 6G
wireless systems to address these challenges is highlighted, and several
research directions that can advance mulsemedia communications are identified
Quality of service and dependability of cellular vehicular communication networks
Improving the dependability of mobile network applications is a complicated task for many reasons: Especially in Germany, the development of cellular infrastructure has not always been fast enough to keep up with the growing demand, resulting in many blind spots that cause communication outages. However, even when the infrastructure is available, the mobility of the users still poses a major challenge when it comes to the dependability of applications: As the user moves, the capacity of the channel can experience major changes. This can mean that applications like adjustable bitrate video streaming cannot infer future performance by analyzing past download rates, as it will only have old information about the data rate at a different location.
In this work, we explore the use of 4G LTE for dependable communication in mobile vehicular scenarios. For this, we first look at the performance of LTE, especially in mobile environments, and how it has developed over time. We compare measurements performed several years apart and look at performance differences in urban and rural areas. We find that even though the continued development of the 4G standard has enabled better performance in theory, this has not always been reflected in real-life performance due to the slow development of infrastructure, especially along highways.
We also explore the possibility of performance prediction in LTE networks without the need to perform active measurements. For this, we look at the relationship between the measured signal quality and the achievable data rates and latencies. We find that while there is a strong correlation between some of the signal quality indicators and the achievable data rates, the relationship between them is stochastic, i.e., a higher signal quality makes better performance more probable but does not guarantee it. We then use our empirical measurement results as a basis for a model that uses signal quality measurements to predict a throughput distribution. The resulting estimate of the obtainable throughput can then be used in adjustable bitrate applications like video streaming to improve their dependability.
Mobile networks also task TCP congestion control algorithms with a new challenge: Usually, senders use TCP congestion control to avoid causing congestion in the network by sending too many packets and so that the network bandwidth is divided fairly. This can be a challenging task since it is not known how many senders are in the network, and the network load can change at any time. In mobile vehicular networks, TCP congestion control is confronted with the additional problem of a constantly changing capacity: As users change their location, the quality of the channel also changes, and the capacity of the channel can experience drastic reductions even when the difference of location is very small. Additionally, in our measurements, we have observed that packet losses only rarely occur (and instead, packets are delayed and retransmitted), meaning that loss-based algorithms like Reno or CUBIC can be at a significant disadvantage. In this thesis, we compare several popular congestion control algorithms in both stationary and mobile scenarios. We find that many loss-based algorithms tend to cause bufferbloat and thus overly increase delays. At the same time, many delay-based algorithms tend to underestimate the network capacity and thus achieve data rates that are too low. The algorithm that performed the best in our measurements was TCP BBR, as it was able to utilize the full capacity of the channel without causing bufferbloat and also react to changes in capacity by adjusting its window. However, since TCP BBR can be unfair towards other algorithms in wired networks, its use could be problematic.
Finally, we also propose how our model for data rate prediction can be used to improve the dependability of mobile video streaming. For this, we develop an algorithm for adaptive bitrate streaming that provides a guarantee that the video freeze probability does not exceed a certain pre-selected upper threshold. For the algorithm to work, it needs to know the distribution of obtainable throughput. We use a simulation to verify the function of this algorithm using a distribution obtained through the previously proposed data rate prediction algorithm. In our simulation, the algorithm limited the video freeze probability as intended. However, it did so at the cost of frequent switches of video bitrate, which can diminish the quality of user experience. In future work, we want to explore the possibility of different algorithms that offer a trade-off between the video freeze probability and the frequency of bitrate switches.Die Verbesserung der ZuverlĂ€ssigkeit von mobilen Netzwerk-basierten Anwendungen ist aus vielen GrĂŒnden eine komplizierte Aufgabe: Vor allem in Deutschland war die Entwicklung der Mobilfunkinfrastruktur nicht immer schnell genug, um mit der wachsenden Nachfrage Schritt zu halten. Es gibt immer noch viele Funklöchern, die fĂŒr KommunikationsausfĂ€lle verantwortlich sind. Aber auch an Orten, an denen Infrastruktur ausreichend vorhanden ist, stellt die MobilitĂ€t der Nutzer eine groĂe Herausforderung fĂŒr die ZuverlĂ€ssigkeit der Anwendungen dar: Wenn sich der Nutzer bewegt, kann sich die KapazitĂ€t des Kanals stark verĂ€ndern. Dies kann dazu fĂŒhren, dass Anwendungen wie Videostreaming mit einstellbarer Bitrate die in der Vergangenheit erreichten Downloadraten nicht zur Vorhersage der zukĂŒnftigen Leistung nutzen können, da diese nur alte Informationen ĂŒber die Datenraten an einem anderen Standort enthalten.
In dieser Arbeit untersuchen wir die Nutzung von 4G LTE fĂŒr zuverlĂ€ssige Kommunikation in mobilen Fahrzeugszenarien. Zu diesem Zweck untersuchen wir zunĂ€chst die Leistung von LTE, insbesondere in mobilen Umgebungen, und wie sie sich im Laufe der Zeit entwickelt hat. Wir vergleichen Messungen, die in einem zeitlichen Abstand von mehreren Jahren durchgefĂŒhrt wurden, und untersuchen Leistungsunterschiede in stĂ€dtischen und lĂ€ndlichen Gebieten. Wir stellen fest, dass die kontinuierliche Weiterentwicklung des 4G-Standards zwar theoretisch eine bessere Leistung ermöglicht hat, dass sich dies aber aufgrund des langsamen Ausbaus der Infrastruktur, insbesondere entlang von Autobahnen, nicht immer in der Praxis bemerkbar gemacht hat.
Wir untersuchen auch die Möglichkeit der Leistungsvorhersage in LTE-Netzen, ohne aktive Messungen durchfĂŒhren zu mĂŒssen. Zu diesem Zweck untersuchen wir die Beziehung zwischen der gemessenen SignalqualitĂ€t und den erreichbaren Datenraten und Latenzzeiten. Wir stellen fest, dass es zwar eine starke Korrelation zwischen einigen der SignalqualitĂ€tsindikatoren und den erreichbaren Datenraten gibt, die Beziehung zwischen ihnen aber stochastisch ist, d. h. eine höhere SignalqualitĂ€t macht eine bessere Leistung zwar wahrscheinlicher, garantiert sie aber nicht. Wir verwenden dann unsere empirischen Messergebnisse als Grundlage fĂŒr ein Modell, das die SignalqualitĂ€tsmessungen zur Vorhersage einer Durchsatzverteilung nutzt. Die sich daraus ergebende SchĂ€tzung des erzielbaren Durchsatzes kann dann in Anwendungen mit einstellbarer Bitrate wie Videostreaming verwendet werden, um deren ZuverlĂ€ssigkeit zu verbessern.
Mobile Netze stellen auch TCP Congestion Control Algorithmen vor eine neue Herausforderung: Normalerweise verwenden Sender TCP Congestion Control, um eine Ăberlastung des Netzes durch das Senden von zu vielen Paketen zu vermeiden, und um die Bandbreite des Netzes gerecht aufzuteilen. Dies kann eine schwierige Aufgabe sein, da es nicht bekannt ist, wie viele Sender sich im Netz befinden, und sich die Netzlast jederzeit Ă€ndern kann. In mobilen Fahrzeugnetzen ist TCP Congestion Control mit dem zusĂ€tzlichen Problem einer sich stĂ€ndig Ă€ndernden KapazitĂ€t konfrontiert: Wenn die Benutzer ihren Standort wechseln, Ă€ndert sich auch die QualitĂ€t des Kanals, und die KanalkapazitĂ€t des Kanals kann drastisch sinken, selbst wenn der Unterschied zwischen den Standorten sehr gering ist. DarĂŒber hinaus haben wir bei unseren Messungen festgestellt, dass Paketverluste nur selten auftreten (stattdessen werden Pakete verzögert und erneut ĂŒbertragen), was bedeutet, dass verlustbasierte Algorithmen wie Reno oder CUBIC einen groĂen Nachteil haben können. In dieser Arbeit vergleichen wir mehrere gĂ€ngige Congestion Control Algorithmen sowohl in stationĂ€ren als auch in mobilen Szenarien. Wir stellen fest, dass viele verlustbasierte Algorithmen dazu neigen, einen PufferĂŒberlauf zu verursachen und somit die Latenzen ĂŒbermĂ€Ăig erhöhen, wĂ€hrend viele latenzbasierte Algorithmen dazu neigen, die KanalkapazitĂ€t zu unterschĂ€tzen und somit zu niedrige Datenraten erzielen. Der Algorithmus, der bei unseren Messungen am besten abgeschnitten hat, war TCP BBR, da er in der Lage war, die volle KapazitĂ€t des Kanals auszunutzen, ohne den PufferfĂŒllstand ĂŒbermĂ€Ăig zu erhöhen. Ebenso hat TCP BBR schnell auf KapazitĂ€tsĂ€nderungen reagiert, indem er seine FenstergröĂe angepasst hat. Da TCP BBR jedoch in kabelgebundenen Netzen gegenĂŒber anderen Algorithmen unfair sein kann, könnte seine Verwendung problematisch sein.
SchlieĂlich schlagen wir auch vor, wie unser Modell zur Vorhersage von Datenraten verwendet werden kann, um die ZuverlĂ€ssigkeit des mobilen Videostreaming zu verbessern. Dazu entwickeln wir einen Algorithmus fĂŒr Streaming mit adaptiver Bitrate, der garantiert, dass die Wahrscheinlichkeit des Anhaltens eines Videos eine bestimmte, vorher festgelegte Obergrenze nicht ĂŒberschreitet. Damit der Algorithmus funktionieren kann, muss er die Verteilung des erreichbaren Durchsatzes kennen. Wir verwenden eine Simulation, um die Funktion dieses Algorithmus zu ĂŒberprĂŒfen. Hierzu verwenden wir eine Verteilung, die wir durch den zuvor vorgeschlagenen Algorithmus zur Vorhersage von Datenraten erhalten haben. In unserer Simulation begrenzte der Algorithmus die Wahrscheinlichkeit des Anhaltens von Videos wie beabsichtigt, allerdings um den Preis eines hĂ€ufigen Wechsels der Videobitrate, was die QualitĂ€t der Benutzererfahrung beeintrĂ€chtigen kann. In zukĂŒnftigen Arbeiten wollen wir die Möglichkeit verschiedener Algorithmen untersuchen, die einen Kompromiss zwischen der Wahrscheinlichkeit des Anhaltens des Videos und der HĂ€ufigkeit der Bitratenwechsel bieten
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