Over the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.

Virtual Reality (VR) is gaining popularity and is increasingly adopted across various industries for its potential to deliver immersive and effective skill development. However, we observe that VR training often follows a one-size-fits-all approach. Trainings typically do not adapt to to individual skill levels, which is particularly important in industrial assembly, where user profiles and expertise levels vary widely. To address this, we applied the concept of adaptive learning to VR assembly training, enabling the system to dynamically provide assistance levels when users struggle and gradually reduce support as their proficiency increases. This paper investigates the learning performance and subjective impact of two types of such adaptive approaches and a non-adaptive variant in a VR user study with 36 participants. The results show that adaptive training significantly enhances user experience and reduces perceived workload. At the same time, adaptive VR learning is found to have a positive impact on learning performance (quantified as a reduced number of assembly mistakes after training). In summary, our findings underscore the potential of applying adaptive learning approaches in VR. To guide future research, we propose guidelines to support the practical adoption of adaptive learning in VR training in manufacturing and beyond.

With HTTP Adaptive Streaming (HAS), client-side Adaptive Bitrate (ABR) algorithms drive the (quality-variant) scheduling and downloading of media segments. These ABR algorithms are implemented in the application layer and can therefore base their logic only on relatively coarse and/or inaccurate application-layer metrics. The recently standardized QUIC transport protocol has many userspace implementations, which paves the way for cross-layer optimizations by exposing transport-layer metrics to application-layer algorithms. In this paper, we investigate whether the availability of fine-grained transport-level throughput metrics can positively impact the operation of ABR algorithms and hence the Quality of Experience (QoE) of HAS users in Video on Demand (VoD) settings. Our results show that QUIC-level throughput data can indeed aid ABR algorithms to more accurately predict playout buffer under-runs, which in turn allows the ABR logic to take reactive measures in a timely fashion such that playback stalls can be avoided under challenging network conditions. Overall, our work presents a step towards improving ABR operation via cross-layer data exchange.

This paper demystifies listeners’ wishes with respect to broadcast radio innovation (with a specific focus on radio-mediated music consumption). Our study encompasses an ideation workshop with radio experts, an exploratory survey and a mixed methods empirical evaluation. The empirical evaluation uses two concrete concepts (i.e., letting listeners on-the-fly replace radio content with preferred content and fostering participatory radio production by involving listeners as radio content curators) as a lens to zoom in on the questionable desirability of radio innovation. It is learned that a significant consumer group exists who will stay loyal to broadcast radio even if it does not evolve substantially, whereas others need disruptive incentives to start listening to radio (again). From our results we distill design recommendations to educate the radio production community about how best to approach radio innovation.

QUIC is marketed to hold many advantages over TCP. However, preliminary experimentation has shown that simply running contemporary HTTP Adaptive Streaming (HAS) implementations over QUIC does not improve but actually hurts streaming performance compared to a traditional TCP deployment. We argue that this behavior can be attributed to the amount of TCP specialization that HAS Adaptive BitRate (ABR) algorithms have received over the years. In contrast to TCP (which can be regarded as a "black box"), QUIC actually encompasses all the necessary tools to empower streaming performance optimization (e.g., definition of custom congestion control algorithms, access to transport-layer metrics in the application layer). This however comes at the expense of added complexity which in turn could lead to misinterpretations of the root causes of suboptimal streaming performance. To facilitate research on HTTP adaptive bitrate streaming over QUIC, in this paper we propose a solution towards jointly visualizing transport- and application-layer metrics to allow for a better understanding of HAS streaming performance over various types of transport protocols (i.e., TCP versus QUIC). We see the work presented in this paper as an important stepping stone towards cross-layer optimization of HAS ABR performance to achieve a better overall Quality of Experience (QoE) for streaming users.

Over-the-top (OTT) streaming services like YouTube and Netflix induce massive amounts of video data, hereby putting substantial pressure on network infrastructure. This paper describes a demonstration of the object-based video (OBV) methodology that allows for the quality-variant MPEG-DASH streaming of respectively the background and foreground object(s) of a video scene. The OBV methodology is inspired by research into human visual attention and foveated compression, in that it allows to adaptively and dynamically assign bitrate to those portions of the visual scene that have the highest utility in terms of perceptual quality. Using a content corpus of interview-like video footage, the described demonstration proves the OBV methodology's potential to downsize video bitrate requirements while incurring at most marginal perceptual impact (i.e., in terms of subjective video quality). Thanks to its standards-compliant Web implementation, the OBV methodology is directly and broadly deployable without requiring capital expenditure.

Over-the-top (OTT) streaming services like YouTube and Netflix induce massive amounts of video traffic. To combat the resulting network load, this article empirically explores the use of the object-based video (OBV) methodology that allows for the quality-variant HTTP Adaptive Streaming of respectively the background and foreground object(s) of a video scene. In particular, we study two alternative video object representation methods where the first meticulously follows the object contour, while the second uses axis-aligned bounding box enclosures. We subjectively compare both techniques to traditional, frame-based video compression in the context of live action content featuring talking persons. The resulting mixed methods data shows that (i) OBV-informed users tolerate substantial background quality degradations, and (ii) at an average bitrate reduction of 14 percent, perceptual differences between respectively contour-based OBV and traditional encoding are small or even non-existing for the non-movie content in our corpus. Although our evaluation focuses on interview-like footage, our qualitative data hints that the presented results might be extrapolatable to other video genres. As such, our findings inform content owners and network operators about video bitrate saving opportunities with marginal perceptual impact.

Static light fields are an effective technology to precisely visualize complex inanimate objects or scenes, synthetic and real-world alike, in Augmented, Mixed and Virtual Reality contexts. Such light fields are commonly sampled as a collection of 2D images. This sampling methodology inevitably gives rise to large data volumes, which in turn hampers real-time light field streaming over best effort networks, particularly the Internet. This paper advocates the packaging of the source images of a static light field as a segmented video sequence so that the light field can then be interactively network streamed in a quality-variant fashion using MPEG-DASH, the standardized HTTP Adaptive Streaming scheme adopted by leading video streaming services like YouTube and Netflix. We explain how we appropriate MPEG-DASH for the purpose of adaptive static light field streaming and present experimental results that prove the feasibility of our approach, not only from a networking but also a rendering perspective. In particular, real-time rendering performance is achieved by leveraging video decoding hardware included in contemporary consumer-grade GPUs. Important trade-offs are investigated and reported on that impact performance, both network-wise (e.g., applied sequencing order and segmentation scheme for the source images of the static light field) and rendering-wise (e.g., disk-versus-GPU caching of source images). By adopting a standardized transmission scheme and by exclusively relying on commodity graphics hardware, the net result of our work is an interoperable and broadly deployable network streaming solution for static light fields.

Web performance is a hot topic, as many studies have shown a strong correlation between slow webpages and loss of revenue due to user dissatisfaction. Front and center in Page Load Time (PLT) optimization is the order in which resources are downloaded and processed. The new HTTP/2 specification includes dedicated resource prioritization provisions, to be used in tandem with resource multiplexing over a single, well-filled TCP connection. However, little is yet known about its application by browsers and its impact on page load performance. This article details an extensive survey of modern User Agent implementations, with the conclusion that the major vendors all approach HTTP/2 prioritization in widely different ways, from naive (Safari, IE, Edge) to complex (Chrome, Firefox). We investigate the performance effect of these discrepancies with a full-factorial experimental evaluation involving eight prioritization algorithms, two off-the-shelf User Agents, 40 realistic webpages, and five heterogeneous (emulated) network conditions. We find that in general the complex approaches yield the best results, while naive schemes can lead to over 25% slower median visual load times. Also, prioritization is found to matter most for heavy-weight pages. Finally, it is ascertained that achieving PLT optimizations via generic server-side HTTP/2 re-prioritization schemes is a non-trivial task and that their performance is influenced by the implementation intricacies of individual browsers.

The bulk of contemporary online video traffic is encoded in a traditional manner, hereby neglecting most, if not all, of the semantics of the underlying visual scene. One essential piece of semantic information in the context of video streaming is awareness of the objects that jointly constitute the scene. A canonical example of a benefit associated with such object awareness is the ability to subdivide a video fragment in respectively a background and one or more foreground objects. This paper reports on a pragmatically designed video streaming approach that exploits object-related knowledge in order to improve the real-time adaptability of video streaming sessions (manifested in the form of increased granularity in terms of streaming quality control). The proposed approach is completely compliant with present-day video codecs and HTTP Adaptive Streaming schemes, most notably H.264 and MPEG-DASH. Findings from subjecting the proposed video streaming technique to a comparative subjective evaluation suggest that scenarios exist where the presented approach holds the capacity to improve on traditional streaming in terms of user-perceived video quality.

Video consumption has since the emergence of the medium largely been a passive affair. This paper proposes augmented Omni-Directional Video (ODV) as a novel format to engage viewers and to open up new ways of interacting with video content. Augmented ODV blends two important contemporary technologies: Augmented Video Viewing and 360 degree video. The former allows for the addition of interactive features to Web-based video playback, while the latter unlocks spatial video navigation opportunities. The augmented ODV principle is exposed to developers through a collection of standards-compliant Web interfaces, this way allowing for a wide range of usage scenarios and heterogeneous consumer profiles to be targeted in a broad spectrum of application domains. The paper covers the extensive set of technologies powering the concept, hereby addressing a variety of topics ranging from content authoring and editing to visualization and rendering. Two use case prototypes showcase the expressiveness and practical feasibility of the augmented ODV methodology, while performance evaluation results establish that the augmented ODV implementation is computationally sufficiently lightweight to warrant execution on low-cost PC hardware and even tablet devices.

Traditional video productions fail to cater to the interactivity standards that the current generation of digitally native customers have become accustomed to. This paper therefore advertises the "activation" of the video consumption process. In particular, it proposes to enhance HTML5 video playback with interactive features in order to transform video viewing into a dynamic pastime. The objective is to enable the authoring of more captivating and rewarding video experiences for end-users. The proposed paradigm extends video consumption, much like Augmented Reality (AR) does with the physical world. Given this conceptual analogy, we have adopted the term Augmented Video Viewing (AVV) to denominate our approach. The current AVV implementation embraces two independent yet cooperative video augmentation concepts: enriching video playback with interactive overlay elements, and erecting real-time interaction bridges between video and digital games. A total of four AVV test cases are presented to provide a hint of how our vision can be realized and of the attainable creative results.

Twitter, Social Networking Sites (SNSs); social media; TweetPos; geographic trends; investigative tool

Wireless Sensor Networks (WSNs) are subject to high levels of dynamism arising from changing environmental conditions and application requirements. Reconfiguration allows software functionality to be optimized for current environmental conditions and supports software evolution to meet variable application requirements. Contemporary software modularization approaches for WSNs allow for software evolution at various granularities; from monolithic re-flashing of OS and application functionality, through replacement of complete applications, to the reconfiguration of individual software components. As the nodes that compose a WSN must typically operate for long periods on a single battery charge, estimating the energy cost of software evolution is critical. This paper contributes a generic model for calculating the energy cost of the reconfiguration in WSN. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and find that (i.) our model accurately predicts the energy cost of reconfiguration and (ii.) component-based reconfiguration has a high initial cost, but provides energy savings during software evolution.

Real-time social interaction possibilities are increasingly disappearing from the media consumption process. A decisive contributing factor to this tendency is growing user location disparity. This paper proposes synchronous MediaSharing (sMS), a distributed, purely web-based framework consisting of APIs plus a back-end that enables geographically separated persons to socially consume multimedia content in a synchronized fashion. The sMS service currently resides in a proof-of-concept stage, awaiting qualitative evaluation by means of user experience research methods, yet its underlying network infrastructure as well as the majority of its principal functional components have already been designed, implemented and evaluated. A chief innovation of the system is that its exclusive reliance on open web standards warrants cross-platform support and unlocks seamless content synchronization across the physical and virtual worlds. We outline our vision for the sMS service, motivate the need for such a system, discuss its current implementation, present tentative practical results that confirm the feasibility and validity of our design, and overview the sMS feature roadmap. The sMS functionality has apparent utility in education, training and professional settings, but also in the field of entertainment and the recreational market.

More and more, multimedia services are being accessed via fixed and mobile networks. These services are typically much more sensitive to packet loss, delay and/or congestion than traditional services. In particular, multimedia data is often time critical and, as a result, network issues are not well tolerated and significantly deteriorate the userpsilas quality of experience (QoE). We therefore propose a QoE optimization platform that is able to mitigate problems that might occur at any location in the delivery path from service provider to customer. More specifically, the distributed architecture supports overlay routing to circumvent erratic parts of the network core. In addition, it comprises proxy components that realize last mile optimization through automatic bandwidth management and the application of processing on multimedia flows. In this paper we introduce a transcoding service for this proxy component which enables the transformation of H.264/AVC video flows to an arbitrary bit rate. Through representative experimental results, we illustrate how this addition enhances the QoE optimization capabilities of the proposed platform by allowing the proxy component to compute more flexible and effective bandwidth distributions.

We present the NIProxy, a flexible network intermediary which aims to improve the Quality of Experience (QoE) of users of networked applications by pushing more intelligence into the network. More specifically, the NIProxy is network- as well as application-aware, meaning it has knowledge of both the transportation network and the application(s) it is serving. This dual awareness is exploited to improve user QoE in two complementary ways. First of all, the NIProxy is capable of dynamically distributing a client's available downstream bandwidth over the different network streams generated by a networked application. Secondly, the NIProxy supports multimedia service provision, meaning it can apply services on multimedia streams on behalf of its clients. An important feature of the NIProxy is that its two QoE-improving mechanisms are not isolated entities but instead can interact with each other. A comprehensive discussion of the NIProxy's software architecture is provided and the implementation of an example service, which adds video transcoding functionality to the NIProxy, is described Finally, presented experimental results clearly demonstrate the effectiveness of our approach.

This work describes our efforts in creating a general object interaction framework for dynamic collaborative virtual environments. Furthermore, we increase the realism of the interactive world by using a rigid body simulator to calculate all actor and object movements. The main idea behind our interactive platform is to construct a virtual world using only objects that contain their own interaction information. As a result, the object interactions are application independent and only a single scheme is required to handle all interactions in the virtual world. In order to have more dynamic interactions, we also created a new and efficient way for human users to dynamically interact within virtual worlds through their avatar. In particular, we show how inverse kinematics can be used to increase the interaction possibilities and realism in collaborative virtual environments. This results in a higher feeling of presence for connected users and allows for easy, on-the-fly creation of new interactions. For the distribution of both the interactive objects and the dynamic avatar interactions, we keep the network load as low as possible. To demonstrate the effectiveness of our techniques, we incorporate them into an existing CVE framework.