This demo presents a hardware-in-the-loop (HiL) framework for validating and verifying wireless communication hardware under controlled and repeatable laboratory conditions. The setup integrates modems as transmitters and receivers with a real-time channel emulator. To simplify the hardware interfaces we exchange multi-path component parameters between the channel model and the radio channel emulator, following the proposed structure in the ongoing IEEE P1944 standardization for site-specific radio channel representations. The demo features an urban vehicular communication scenario, demonstrating the capability to replicate realistic propagation conditions with non-stationary propagation conditions. Our HiL test system allows dynamic motion changes of the vehicles based on the received data during the emulation process. Site-specific channel emulation enables the lab-based validation of realistic vehicular applications, 5G and 6G physical layer technologies, and the training and testing of AI/ML based receiver architectures.

Effective communication between vehicles and road users is essential for reducing accidents and congestion. Reli-able wireless communication is crucial for decision-making in advanced driver assistance systems and autonomous vehicles. In this work, we propose a convolutional neural network to predict the frame error rate in vehicle-to-infrastructure scenarios. Using a geometry-based stochastic channel model and hardware-in-the-loop emulation, we generate a dataset on which our model achieves 90 % validation accuracy. To adapt the model to new data, such as vehicle-to-vehicle scenarios, and to reduce computational costs for retraining the entire model from scratch, we explore methods like fine-tuning, transfer learning, and learning without forgetting (LwF). While these methods improve performance on new data, they reduce accuracy on the original data. To address this, we modify LwF by including some original data, achieving a balanced accuracy of 81.96%.

Cooperative connected automated mobility depends on sensing and wireless communication functions. With increasing carrier frequency both functions can be realized with the same hardware, however, the attenuation of radio signals increases quadratically with the carrier frequency. Hence, link setup becomes challenging in vehicular scenarios due to the required beam finding process. In this paper we investigate the multipath components of the vehicle-to-infrastructure (V2I) radio channel in three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of $31.25 \mu \mathrm{~s}$. The channel impulse responses are collected simultaneously at all three carrier frequencies. Using the high temporal sampling rate we apply the CLEAN algorithm, enabling the estimation of the weight, delay and Doppler frequency of multipath components. By analyzing the collinearity of the Doppler normalized scattering function between the frequency bands we found that the collinearity between the 3.2 GHz and 34.3 GHz band as well as between the 3.2 GHz and 62.35 GHz is smaller in the non-line of sight (NLOS) region but increases for the line-of-sight (LOS).

Video moment retrieval aims to locate the timestamps best matching the query description within an untrimmed video. However, existing video moment retrieval approaches typically suffer from two major limitations: (1)Utilize only negative moment-sentence pairs sampled from intra-videos, which may overfit the bias of the dataset and not have an excellent understanding of the video and query due to the dataset size and annotation biases. (2)Decouple the video and the query, perform unimodal learning separately, and then concatenate them together as multimodal fusion features. In this paper, we propose a novel approach named Momentum Contrastive Matching Network(MCMN). Inspired by MoCo, we propose the Momentum Cross-modal Contrast for cross-modal learning to enable large-scale negative sample interactions, which contributes to the generation of more precise and discriminative representations, and use temporal decay to model key attenuation in the memory queue when computing the contrastive loss. In addition, we use an attention module to adaptively generate clip-specific word embeddings to achieve semantic alignment from a temporal perspective, which are considered to be more important for finding relevant video contents with large boundary ambiguities. Experimental results on the three major video moment retrieval benchmark datasets, including TACoS, Charades-STA, and ActivityNet Captions demonstrate that MCMN surpasses previous methods and reaches state-of-the-art with disparate visual features.

Wireless vehicular communication will increase the safety of road users. The reliability of vehicular communication links is of high importance as links with low reliability may diminish the advantage of having situational traffic information. The goal of our investigation is to obtain a reliable coverage area for non-stationary vehicular scenarios. Therefore we propose a deep neural network (DNN) for predicting the expected frame error rate (FER). The DNN is trained in a supervised fashion, where a time-limited sequence of channel frequency responses has been labeled with its corresponding FER values assuming an underlying wireless communication system, i.e. IEEE 802.11p. For generating the training dataset we use a geometry-based stochastic channel model (GSCM). We obtain the ground truth FER by emulating the time-varying frequency responses using a hardware-in-the-loop setup. Our GSCM provides the propagation path parameters which we use to fix the statistics of the fading process at one point in space for an arbitrary amount of time, enabling accurate FER estimation. Using this dataset we achieve an accuracy of 85% of the DNN. We use the trained model to predict the FER for measured time-varying channel transfer functions obtained during a measurement campaign. We compare the predicted output of the DNN to the measured FER on the road and obtain a prediction accuracy of 78%.

In an world shifting towards wireless communications, the already scarce electromagnetic spectrum within the unlicensed bands is becoming increasingly crowded. All wireless devices operating in those bands need to co-exist without interfering with each other. Frequency hopping spread spectrum (FHSS) is a communication technique especially resilient to interference due to its constant change of the carrier frequency and its narrowband transmission bandwidth. Furthermore, it produces minimal interference to other signals in the same frequency band using wider bandwidth. However, interference can also be harmful even for FHSS transmissions as a result of the loaded ISM bands. Intelligent spectrum sensing techniques can contribute to a more efficient spectral usage. In this paper, we propose a supervised learning algorithm which predicts the future time-frequency location of a FHSS signal. We design a convolutional neural network which is trained on a dataset, obtained from measurements of two FHSS sources. Based only on a small observation window of 50 ms, it predicts the signal appearance of the following 25 ms in a time-frequency representation. To show that we can accurately predict the signal, we introduce a special score measure. The mean score of about 0.9 with small standard deviation demonstrates the high fidelity prediction of the signal’s evolution.

In this paper we present a hardware-in-the-loop (HiL) framework for testing wireless vehicle-to-everything (V2X) communication hardware, i.e., modems under realistic channel conditions. The framework includes a wireless channel emulator, which is capable of emulating non-stationary wireless channels in real-time. We validate the HiL framework by comparing the frame error rate (FER) obtained via emulation with data obtained during a V2X measurement campaign using the same IEEE 802.11p based modems. To do this we acquire measured time-variant channel transfer function and FER measurements simultaneously. The results show that our HiL approach is feasible and that we can obtain FER measurements in the laboratory that closely match the measurement results obtained on the road, giving the maximal distance of 0.099 between their cumulative distribution functions.

In this paper we present dual-band massive multiple-input multiple-output (MIMO) channel measurements for a railway station scenario. The massive MIMO link shall provide an ultra-reliable low-latency communication link from the control center to the locomotive. In the measurement campaign the locomotive moves from line-of-sight (LOS) to non line-of-sight. We present dual frequency band measurements, where the massive MIMO array at the base station has 24 receive antenna elements at 1890 MHz and 8 receive antenna elements at 748 MHz. The measurement bandwidth is 20 MHz and we use a repetition rate of 1 ms to acquire the time-variant channel frequency response from the locomotive to all 32 antenna elements in parallel. We provide a first analysis of the root mean square (RMS) delay spread, the path loss coefficients and the channel hardening in both frequency bands. The measurements show that the RMS delay spread at lower frequencies is slightly smaller compared to higher frequencies, due to a stronger LOS component at lower frequencies. Furthermore, the results indicate that increasing the number of receive antennas in vertical domain, does not allow for better channel hardening and that lower frequencies allow for better channel hardening with the same amount of antennas.

This paper discusses a freely available and open dataset containing vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) OFDM-based wireless channel measurement data including synchronised sensor data such as radar, LiDAR and high precision GPS. The wireless channel measurement is conducted at the carrier frequencies of 3.2 GHz and 5.81 GHz which are the most promising frequency bands in which future V2X communication systems will operate. The dataset contains the wireless channel measurement data of various V2X scenarios along with synchronized sensor information from a vehicle. In addition to the wireless channel measurement data, the dataset also includes frame error rate measurements from a IEEE 802.11p based communication system, synchronized to the other measurement data.

In this paper we present and discuss results of a wireless vehicle-to-vehicle (V2V) dualband channel measurement campaign at center frequencies of 3.2 GHz and 34.3 GHz in an urban street scenario. The measurement is conducted using a bandwidth of 155.5 MHz and a sounding repetition rate of 62.5 $\mu \mathrm{s}$ for both bands. At the transmitter side we use omni-directional antennas and at the receiver side directional antennas with 17° opening angles. With this setup, we present the first comparison of simultaneous and dynamic multiband V2V measurements using the time-variant power delay profile (PDP) and the Doppler spectral density (DSD). We find close similarities for the line-of-sight, specular as well as diffuse reflections in both frequency bands, enabling future work for out-of-band beam finding in vehicular mmWave systems.

In this paper we present a software defined radio (SDR) based measurement framework that allows for simultaneous measurements of wireless communication channels over multiple bands. We present and discuss results of a wireless vehicular-to-infrastructure multiband channel measurement campaign at center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz in a street crossing scenario. The measurement is conducted using a bandwidth of 155.5 MHz and a sounding repetition rate of 31.25 μs. We compare the measurements using the time-variant power delay profile (PDP) and the Doppler spectral density (DSD).

In this paper we adopt and modify a well-known locality-aware hashing scheme to the problem of active stochastic scatterer selection in vehicular non-stationary geometry-based stochastic channel models (GSCM). We show, how under relaxed assumptions on the query set an efficient selection of active stochastic scatterers during simulation is computationally feasible. The proposed approach enables real-time simulation and emulation of large-scale GSCMs by restricting the active stochastic scatterer set to meet given resource constraints. We showcase our approach by introducing a GSCM that is boot-strapped via OpenStreetMap data. The stochastic scatterers are placed automatically along buildings, traffic signs and vegetation. We validate and investigate the impact of the proposed approach on the accuracy of a GSCM by means of second order statistics of the time- and frequency-varying fading process. For validation and performance evaluation we parameterize our GSCM using a vehicular wireless channel measurement campaign conducted in the inner city of Vienna. The impact of selecting only a subset of scatterers is then evaluated using the calibrated GSCM.

In this paper we introduce the AIT ray tracer (RT) that is able to accurately simulate propagation effects of wireless communication channels. It covers the classical propagation mechanisms such as specular reflections, transmission and edge-diffraction. The novelty of our RT is that it allows to obtain correlated diffuse scattering and, thus, provides a time-variant Doppler spectral density. This is achieved by introducing a lattice-based surface tiling, which does not alter the position of the scattering points during simulation but provides high resolution diffuse scattering. The selection of the basis vectors for the finite lattice ensures that each ray falls into a separate delay bin (at least for first-order scattering). We implement our RT using the NVIDIA OptiX ray tracing engine, this enables us to trace a vast amount of rays in a single pass. We compare our RT simulations to a measurement conducted in a non-stationary urban intersection scenario achieving a near perfect match in terms of second order statistics.

Repeatable system-level test methods for vehicle-to-everything communication (V2X) are crucial in improving road safety and supporting connected autonomous driving. They rely on geometrical channel models that provide realistic signal propagation delays, Doppler shifts and path loss. In this work we focus on the validation of an OpenStreetMap (OSM) geometry based stochastic channel model (GSCM) that has a high degree of automation and low computational complexity. In an urban intersection scenario we compare the time-variant power delay profile (PDP) and Doppler spectral density (DSD) obtained from a measurement campaign to the ones obtained by the OSM-GSCM. Furthermore, we measure packet error rates (PER) of commercial modem hardware by connecting a transmitter and a receiver to the AIT channel emulator, i.e. using a hardware-in-the-loop (HiL) setup. We compare the PER obtained when the channel emulator uses the OSM-GSCM as input and when it uses the measured impulse responses. The OSM-GSCM path loss shows minor differences to the measurements due to the 2D structure of the OSM-GSCM, while the PER, PDP and DSD exhibit a good match between OSM-GSCM and empirical measurement data.

The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.

The performance of wireless communication systems is fundamentally determined by the properties of the underlying wireless communication channel. Vehicular communication channels exhibit time-variant multi-path propagation with non-stationary channel statistics. Thus, channel emulation tools for the reproducible test of wireless communication systems are urgently needed to enable the development of ultra-reliable low-latency communication links. In this paper we validate the vehicle-in-the-loop (ViL) test of vehicle-to-everything (V2X) communication links by means of time-variant channel emulation. The validation is performed by comparing the received signal strength indicator (RSSI) and packet error rate (PER) of measurements on a proving ground with the RSSI and PER obtained from ViL tests. For the ViL tests, the wireless communication channel is emulated using a geometry-based stochastic channel model, which is updated in real-time, dependent on the position and velocity of the vehicles. We collect results of different scenarios on the proving ground and from ViL tests. The results show an qualitative match between ViL test and measurement on the proving ground. An exact quantitative match can be obtained with the calibration parameters from the measurements.

Forecasts predict that Internet traffic will continue to grow in the near future. A huge share of this traffic is caused by multimedia streaming. The quality of experience (QoE) of such streaming services is an important aspect and in most cases the goal is to maximize the bit rate which-in some cases-conflicts with the requirements of both consumers and providers. For example, in mobile environments users may prefer a lower bit rate to come along with their data plan. Likewise, providers aim at minimizing bandwidth usage in order to reduce costs by transmitting less data to users while maintaining a high QoE. Today's adaptive video streaming services try to serve users with the highest bit rates that consequently results in high QoE. In practice, however, some of these high bit rate representations may not differ significantly in terms of perceived video quality compared to lower bit rate representations. In this paper, we present a novel approach to determine the statistically indifferent quality variation of adjacent video representations for adaptive video streaming services by adopting standard objective quality metrics and existing QoE models. In particular, whenever the quality variation between adjacent representations is imperceptible from a statistical point of view, the representation with higher bit rate can be substituted with a lower bit rate representation. As expected, this approach results in savings with respect to bandwidth consumption while still providing a high QoE for users. The approach is evaluated subjectively with a crowdsourcing study. Additionally, we highlight the benefits of our approach, by providing a case study that extrapolates possible savings for providers.

Forwarding decisions in classical IP-based networks are predetermined by routing. This is necessary to avoid loops, inhibiting opportunities to implement an adaptive and intelligent forwarding plane. Consequently, content distribution efficiency is reduced due to a lack of inherent multi-path transmission. In Named Data Networking (NDN) instead, routing shall hold a supporting role to forwarding, providing sufficient potential to enhance content dissemination at the forwarding plane. In this paper, we design, implement, and evaluate a novel probability-based forwarding strategy, called Stochastic Adaptive Forwarding (SAF) for NDN. SAF imitates a self-adjusting water pipe system, intelligently guiding and distributing interests through network crossings circumventing link failures and bottlenecks. Just as real pipe systems, SAF employs overpressure valves enabling congested nodes to lower pressure autonomously. Through an implicit feedback mechanism, it is ensured that the fraction of the traffic forwarded via congested nodes decreases. By conducting simulations, we show that our approach outperforms existing forwarding strategies in terms of the interest satisfaction ratio in the majority of the evaluated scenarios. This is achieved by extensive utilization of NDN's multipath and content-lookup capabilities without relying on the routing plane. SAF explores the local environment by redirecting requests that are likely to be dropped anyway. This enables SAF to identify new paths to the content origin or to cached replicas, circumventing link failures, and resource shortages without relying on routing updates.

Today, the global share and increase of Internet traffic is largely caused by multimedia delivery, mainly encompassing video, audio and image sharing on social, news, and entertainment platforms. This fact is well known to the Internet research community, which tries to counteract by increasing the content delivery efficiency. So-called Information-Centric Networks (ICN) are of considerable interest, advertised as enablers for intelligent networks, where effective delivery is to be provided as an inherent network feature. Most research proposals in this area are evaluated in simulated environments, using simulation frameworks such as OMNeT++ or ns-3. However, simulations always have shortcomings and cannot substitute measurements in physical networks. In this demonstration, we show how to readily set up an ICN-based testbed using low-budget single-board computers to conduct comprehensive emulations. We choose the scenario of pull-based adaptive video delivery as a showcase and evaluate the performance of different client-based adaptation mechanisms at the application level and different content forwarding strategies at the network level. All of the presented tools and visualization features are provided as open source contributions to the community.

Adaptive streaming strategies over HTTP allow to serve heterogeneous video users with varying demands. By providing different encoded versions (representations) of each video sequence on the server, clients have the freedom to select a representation that best fits their needs. While the topic of selecting a representation based on a pre-defined set is covered very well in the literature, the problem of how to properly select the representation set stored at the main server is usually an overlooked challenge. In this work, we provide an analysis on how the choice of representations on the server impacts the clients' quality. This is achieved by conducting NS-3 based simulations with a total of 10k users and up to 300 concurrent DASH clients for several recommended sets (e.g., Netflix, YouTube, and Apple), and measuring the experienced quality over a timespan of 24 hours. The results show that under heavy load (at peak hours) there is still room for improvement.

Adaptive content delivery is the state of the art in real-time multimedia streaming. Leading streaming approaches, e.g., MPEG-DASH and Apple HTTP Live Streaming (HLS), have been developed for classical IP-based networks, providing effective streaming by means of pure client-based control and adaptation. However, the research activities of the Future Internet community adopt a new course that is different from today's host-based communication model. So-called information-centric networks are of considerable interest and are advertised as enablers for intelligent networks, where effective content delivery is to be provided as an inherent network feature. This paper investigates the performance gap between pure client-driven adaptation and the theoretical optimum in the promising Future Internet architecture named data networking (NDN). The theoretical optimum is derived by modeling multimedia streaming in NDN as a fractional multi-commodity flow problem and by extending it taking caching into account. We investigate the multimedia streaming performance under different forwarding strategies, exposing the interplay of forwarding strategies and adaptation mechanisms. Furthermore, we examine the influence of network inherent caching on the streaming performance by varying the caching polices and the cache sizes.

Social networks have become ubiquitous and with these new possible ways for social communication and experiencing multimedia together the traditional TV scenario drifts more and more towards a distributed social experience. Asynchronism in the multimedia playback of the users may have a significant impact on the acceptability of systems providing the distributed multimedia experience. The synchronization needed in such systems is called Inter-Destination Multimedia Synchronization. In this paper we propose a demo that implements IDMS by the means of our self-organized and distributed approach assisted by pull-based streaming. We also provide a video of the planned demonstration and provide the mobile application as open source licensed under the GNU LGPL.

As social networks have become more pervasive, they have changed how we interact socially. The traditional TV experience has drifted from an event at a fixed location with family or friends to a location-independent and distributed social experience. In addition, more and more Video On-Demand services have adopted pull-based streaming. In order to provide a synchronized and immersive distributed TV experience we introduce self-organized Inter-Destination Multimedia Synchronization (IDMS) for adaptive media streaming. In particular, we adapt the principles of IDMS to MPEG-DASH to synchronize multimedia playback among geographically distributed peers. We introduce session management to MPEG-DASH and propose a Distributed Control Scheme (DCS) to negotiate a reference playback timestamp among the peers participating in an IDMS session. We evaluate our DCS with respect to scalability and the time required to negotiate the reference playback timestamp. Furthermore, we investigate how to compensate for asynchronism using Adaptive Media Playout (AMP) and define a temporal distortion metric for audio and video which allows the impact of playback rate variations to be modeled with respect to QoE. This metric is evaluated based on a subjective quality assessment using crowdsourcing.

Mobile peer-to-peer (P2P) computing with applications such as for video on demand, le sharing, and video conferencing is gaining momentum based on new standards and technologies such as IETF PPSP, WiFi-Direct and BitTorrent live streaming. In this paper, we describe the mobile system MyMedia,

Inter-Destination Multimedia Synchronization (IDMS) pushes social interactions to a new level. IDMS allows the users to experience multimedia together with friends, colleagues, or the family while having a real-time communication at the same time. The actual challenge of synchronizing the playout of each participant to a reference playout time is a tough task in terms of Quality of Experience (QoE). A possible solution for carrying out the synchronization is Adaptive Media Playout (AMP) where the playout speed of the multimedia is increased or decreased. In this paper we evaluate the impact of the playout variations on the QoE by adopting a crowdsourcing approach. In particular, we investigate the impact of randomly selecting content sections for adapting the playout rate compared to our approach that exploits audio-visual features of the content in order to minimize the impact on the QoE.

Current QoE research is mainly focusing on single modalities (audio, visual) or combinations thereof. In our research, we propose annotating traditional multimedia content with additional sensory effects, such as ambient light, vibration, wind, and olfaction, which could potentially stimulate all human senses. Investigating the influence of individual sensory effects and combinations thereof is important in order to understand how these individual sensory effects influence the Quality of Experience (QoE) as a whole. In this article, we describe the results of such a subjective quality assessment of audio-visual sequences which are annotated with additional sensory effects such as ambient light, wind, and vibration using the MPEG-V standard. The results of this assessment allow us to derive a utility model representing the Quality of Sensory Experience (QuaSE) complementary to existing QoE models described in terms of Quality of Service (QoS) parameters. For validating our proposed utility model, we provide an example instantiation and validate it against results of subjective quality assessments.

Secure communication is often based on encryption thus hinges on (public-key) infrastructures that handle all the key-management. This inevitably requires human intervention, thus creating a rather vulnerable point in the system. So it appears desirable to automate key-management tasks to the widest possible extent. In this work, we report on a software implementation of secure multipath transmission. Our system takes a network infrastructure model as input and determines the maximal achievable security for a communication between a chosen sender and receiver, while handling all key-management transparently for the user. The security is information-theoretic, and unlike public-key or symmetric cryptography does neither hinge on computational intractability nor empirical evidence. More importantly, security can be measured in quantitative terms, thus making the results useful in enterprise risk management. Our software computes the risk for a given transmission under multipath transmission and generates simple \textsc{OmNet++} models to demonstrate the channel construction as practically doable and to measure the additional network overhead. This is for a-priori decision-support and practical guidance for an installation of secure multipath transmission as a high-security transmission service within the enterprise network.

In this paper, we present our implementation and evaluation of Dynamic Adaptive Streaming over Content centric networking (DASC) which implements MPEG Dynamic Adaptive Streaming over HTTP (DASH) utilizing a Content Centric Networking (CCN) naming scheme to identify content segments in a CCN network. In particular, video segments formatted according to MPEG-DASH are available in different quality levels but instead of HTTP, CCN is used for referencing and delivery. Based on the conditions of the network, the DASC client issues interests for segments achieving the best throughput. Due to segment caching within the network, subsequent requests for the same content can be served quicker. As a result, the quality of the video a user receives progressively improves, effectively overcoming bottlenecks in the network. We present two sets of experiments to evaluate the performance of DASC showing that throughput indeed improves. However, the generated overhead is relatively large and the adaptation strategy used for DASH that assumes an end-to-end connection could be revised for the hop-by-hop architecture of CCN.

This paper presents the usage of CCN, which is a candidate for the next-generation Internet, in combination with the new Dynamic Adaptive Streaming over HTTP (DASH) standard, which was recently ratified by ISO/IEC MPEG. In contrast to the Internet Protocol, which is mainly based on the host-to-host connection paradigm originated in the 1970s, Content Centric Networking (CCN) focuses on the content itself, instead of its location. Considering the dominance of multimedia traffic in todays' Internet, the streaming performance of DASH over CCN as well as the problems introduced by this combination is worth to be investigated in detail. Therefore, we evaluate the protocol overhead introduced by the usage of CCN compared to the HTTP versions 1.0 and 1.1. Furthermore, the performance of DASH over CCN under different network conditions is compared to the performance of HTTP 1.0/1.1. Our results showed that although CCN comes together with higher protocol overhead than HTTP 1.0/1.1 as well as a prototype implementation, it can definitely compete with HTTP 1.0 in media streaming. Based on the evaluation results, problems as well as improvement possibilities are identified, which are the basis for future work in this area.

This paper describes a toolset for the authoring, simulating, and rendering of multimedia content annotated with Sensory Effect Metadata (SEM) descriptions as specified in Part 3 of the MPEG V standard. This part of MPEG-V standardizes the description of sensory effects (e.g., light, wind) in order to be rendered on sensory devices (e.g., fans, vibration chairs) aiming at generating a sensory experience stimulating possibly all human senses. Our implementation comprises a toolset to author sensory effects associated with multimedia content and the simulation thereof. Furthermore, it includes a library, a standalone player, and a Web browser plug-in which enables the playback and rendering of sensory effects on off-the-shelf rendering devices and in various contexts. All software modules are available under the GNU General Public License (GPL) v3 and the GNU Lesser General Public License (LGPL) v3 respectively.

In this paper we demonstrate our MPEG-DASH evaluation suite, which comprises several components on the client side as well as on the server side. The major client components are the VLC DASH plugin, libDASH, and DASH-JS, a JavaScript-based DASH client. These tools enable performance tests on various platforms, e.g., Windows and Linux as well as mobile platforms such as Android. Moreover, due to their flexible structure it is possible to integrate adaptation logics and evaluate them under consistent conditions. On the server side we provide the content generation tool DASHEncoder, our MPEG-DASH datasets well as the MPEG-DASH conformance validator.