Designers of storytelling experiences in virtual reality (VR) can take advantage of the medium's realism and immersion to communicate their intentions.However, interaction freedom comes with unpredictability, raising the risk of miscommunication between the experience sought by the designer and the player's interpretation.To better understand such miscommunications, we revisit Don Norman's work on stages of action to propose a model of designerplayer gulfs in VR that incorporates eight classes of communication gulfs.We designed a two-phase study where 10 participants designed VR scenarios and then played scenarios created by previous participants.Through coupled structured interviews, we identified 127 issues in VR-mediated communication that were mapped to our model to understand their impact on the player's interpretation of the narrative experience.Our work provides a roadmap to identifying sources of miscommunication in VR, a first step to conceiving principles and guidelines for achieving effective communication in storytelling experiences.

Concept-based models have been proposed as a new line of research for explainable by-design deep learning models. However, those models show their whole power when applied to benchmarks where the concepts are well defined and the concepts' attributes easily extractable from the raw data. In this paper, we challenge the most recent concept-based model initially developed for image classification, on more complex interpretative tasks from a recently proposed video benchmark where they perform poorly. We conduct a root cause analysis of the poor performances of state-of-the-art explainable concept-based models for these multimodal interpretative tasks, and propose adaptations to design robust explainable models for detecting character objectification in this novel challenging video benchmark. We show that the optimal architectural choice may vary depending on the modality setting, thereby showing that designing multimodal concept-based approaches remains an open challenge and calls for further investigation.

This paper addresses the problem of dynamic packet routing in overlay networks using a fully decentralized Multi-Agent Deep Reinforcement Learning (MA-DRL). Overlay networks are built by having a virtual topology on top of an Internet Service Provider (ISP) underlay network, where those nodes are running a fixed, single path routing policy decided by the ISP. In such a scenario, the underlay topology and the traffic are unknown by the overlay network. In this setting, we propose O-DQR, which is an MA-DRL framework working under Distributed Training Decentralized Execution (DTDE), where the agents are allowed to communicate only with their immediate overlay neighbors during both training and inference. We address three fundamental aspects for deploying such a solution: (i) performance (delay, loss rate), where the framework can achieve near-optimal performance, (ii) control overhead, which is reduced by enabling the agents to send control packets only when needed dynamically; and (iii) training convergence stability, which is improved by proposing a guided reward mechanism for dynamically learning the penalty applied when a packet is lost. Finally, we evaluate our solution through extensive experimentation in a realistic network simulation in both offline training and continual learning settings.

In film gender studies, the concept of “male gaze” refers to the way the characters are portrayed on-screen as objects of desire rather than subjects. In this article, we introduce a novel video-interpretation task, to detect character objectification in films. The purpose is to reveal and quantify the usage of complex temporal patterns operated in cinema to produce the cognitive perception of objectification. We introduce the ObyGaze12 dataset, made of 1914 movie clips densely annotated by experts for objectification concepts identified in film studies and psychology. We evaluate recent vision models, show the feasibility of the task and where the challenges remain with concept bottleneck models. Our new dataset and code are made available to the community.

Virtual Reality (VR) technologies enable strong emotions compared to traditional media, stimulating the brain in ways comparable to real-life interactions. This makes VR systems promising for research and applications in training or rehabilitation, to imitate realistic situations. Nonetheless, the evaluation of the user experience in immersive environments is daunting, the richness of the media presents challenges to synchronise context with behavioural metrics in order to provide fine-grained personalised feedback or performance evaluation. The variety of scenarios and interaction modalities multiplies this difficulty of user understanding in face of lifelike training scenarios, complex interactions, and rich context.We propose a task-based methodology that provides fine-grained descriptions and analyses of the experiential user experience (UX) in VR that (1) aligns low-level tasks (i.e. take an object, go somewhere) with multivariate behaviour metrics: gaze, motion, skin conductance, (2) defines performance components (i.e., attention, decision, and efficiency) with baseline values to evaluate task performance, and (3) characterises task performance with multivariate user behaviour data. To illustrate our approach, we apply the task-based methodology to an existing dataset from a road crossing study in VR. We find that the task-based methodology allows us to better observe the experiential UX by highlighting fine-grained relations between behaviour profiles and task performance, opening pathways to personalised feedback and experiences in future VR applications.

Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple head motion prediction for 360° video streaming. We introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. We design a training procedure to obtain a flexible, lightweight stochastic prediction model compatible with sequence-to-sequence neural architectures. Experimental results on four different datasets show that DVMS outperforms competitors adapted from the self-driving domain by up to 41% on prediction horizons up to 5 s, at lower computational and memory costs. To understand how the learned features account for the motion uncertainty, we analyze the structure of the learned latent space and connect it with the physical properties of the trajectories. We also introduce a method to estimate the likelihood of each generated trajectory, enabling the integration of DVMS in a streaming system. We hence deploy an extensive evaluation of the interest of our DVMS proposal for a streaming system. To do so, we first introduce a new Python-based 360° streaming simulator that we make available to the community. On real-world user, video, and networking data, we show that predicting multiple trajectories yields higher fairness between the traces, the gains for 20–30% of the users reaching up to 10% in visual quality for the best number K of trajectories to generate.

Adaptive bitrate (ABR) algorithms are used in streaming media to adjust video or audio quality based on the viewer's network conditions to provide a smooth playback experience. With the rise of virtual reality (VR) headsets, 360° video streaming is growing rapidly and requires efficient ABR strategies to also adapt the video quality to the user's head position. However, research in this field is often difficult to compare due to a lack of reproducible simulations. To address this problem, we provide SMART360, a 360° streaming simulation environment to compare motion prediction and adaptive bitrates strategies. We provide sample inputs and baseline algorithms along with the simulator, as well as examples of results and visualizations that can be obtained with SMART360. The code and data are made publicly available.

Virtual Reality (VR) technology enables “embodied interactions” in realistic environments where users can freely move and interact, with deep physical and emotional states. However, a comprehensive understanding of the embodied user experience is currently limited by the extent to which one can make relevant observations, and the accuracy at which observations can be interpreted.

While immersive media have been shown to generate more intense emotions, saliency information has been shown to be a key component for the assessment of their quality, owing to the various portions of the sphere (viewports) a user can attend. In this article, we investigate the tri-partite connection between user attention, user emotion and visual content in immersive environments. To do so, we present a new dataset enabling the analysis of different types of saliency, both low-level and high-level, in connection with the user’s state in 360◦videos. Head and gaze movements are recorded along with self-reports and continuous physiological measurements of emotions. We then study how the accuracy of saliency estimators in predicting user attention depends on user-reported and physiologically-sensed emotional perceptions. Our results show that high-level saliency better predicts user attention for higher levels of arousal. We discuss how this work serves as a first step to understand and predict user attention and intents in immersive interactive environments.

From a user perspective, immersive content can elicit more intense emotions than flat-screen presentations. From a system perspective, efficient storage and distribution remain challenging, and must consider user attention. Understanding the connection between user attention, user emotions and immersive content is therefore key. In this article, we present a new dataset, PEM360 of user head movements and gaze recordings in 360° videos, along with self-reported emotional ratings of valence and arousal, and continuous physiological measurement of electrodermal activity and heart rate. The stimuli are selected to enable the spatiotemporal analysis of the connection between content, user motion and emotion. We describe and provide a set of software tools to process the various data modalities, and introduce a joint instantaneous visualization of user attention and emotion we name Emotional maps. We exemplify new types of analyses the PEM360 dataset can enable. The entire data and code are made available in a reproducible framework.

This paper contains the research proposal of Quentin Guimard that was presented at the MMSys 2022 doctoral symposium.

Prediction of head movements in immersive media is key to design efficient streaming systems able to focus the bandwidth budget on visible areas of the content. Numerous proposals have therefore been made in the recent years to predict 360° images and videos. However, the performance of these models is limited by a main characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. Our method provides likelihood estimates of every predicted trajectory, enabling direct integration in streaming optimization. To the best of our knowledge, this is the first work that considers the problem of multiple head motion prediction for 360° video streaming. We first quantify this uncertainty from the data. We then introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. We design a training procedure to obtain a flexible and lightweight stochastic prediction model compatible with sequence-to-sequence recurrent neural architectures. Experimental results on 3 different datasets show that our method DVMS outperforms competitors adapted from the self-driving domain by up to 37% on prediction horizons up to 5 sec., at lower computational and memory costs. Finally, we design a method to estimate the respective likelihoods of the multiple predicted trajectories, by exploiting the stationarity of the distribution of the prediction error over the latent space. Experimental results on 3 datasets show the quality of these estimates, and how they depend on the video category.

Virtual reality (VR) offers opportunities in human-computer interaction research, to embody users in immersive environments and observe how they interact with 3D scenarios under well-controlled environments. VR content has stronger influences on users physical and emotional states as compared to traditional 2D media, however, a fuller understanding of this kind of embodied interaction is currently limited by the extent to which attention and behavior can be observed in a VR environment, and the accuracy at which these observations can be interpreted as, and mapped to, real-world interactions and intentions. This thesis aims at the creation of a system to help designers in the analysis of the entire user experience in VR environment: how they feel, what is their intentions when interacting with a certain object, provide them guidance based on their needs and attention. A controlled environment in which the user is guided will help to establish a better intersubjectivity between designer intention who created the experience and users who lived it and will lead to a more efficient analysis of the user behavior in VR systems for the design of better experiences.

Virtual reality (VR) offers extraordinary opportunities in user behavior research to study and observe how people interact in immersive 3D environments. A major challenge of designing these 3D experiences and user tasks, however, lies in bridging the inter-relational gaps of perception between the designer, the user, and the 3D scene. Paul Dourish identified three gaps of perception: ontology between the scene representation and the user and designer interpretation, intersubjectivity of task communication between designer and user, and intentionality between the user's intentions and designer's interpretations. We present the GUsT-3D framework for designing Guided User Tasks in embodied VR experiences, i.e., tasks that require the user to carry out a series of interactions guided by the constraints of the 3D scene. GUsT-3D is implemented as a set of tools that support a 4-step workflow to (1) annotate entities in the scene with navigation and interaction possibilities, (2) define user tasks with interactive and timing constraints, (3) manage interactions, task validation, and user logging in real-time, and (4) conduct post-scenario analysis through spatio-temporal queries using ontology definitions. To illustrate the diverse possibilities enabled by our framework, we present two case studies with an indoor scene and an outdoor scene, and conducted a formative evaluation involving six expert interviews to assess the framework and the implemented workflow. Analysis of the responses show that the GUsT-3D framework fits well into a designer's creative process, providing a necessary workflow to create, manage, and understand VR embodied experiences.

videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning approaches for this problem and identify hidden flaws from a thorough root-cause analysis. Second, from the results of this analysis, we design a new proposal establishing state-of-the-art performance. First, re-assessing the existing methods that use both modalities, we obtain the surprising result that they all perform worse than baselines using the user's trajectory only. A root-cause analysis of the metrics, datasets and neural architectures shows in particular that (i) the content can inform the prediction for horizons longer than 2 to 3 sec. (existing methods consider shorter horizons), and that (ii) to compete with the baselines, it is necessary to have a recurrent unit dedicated to process the positions, but this is not sufficient. Second, from a re-examination of the problem supported with the concept of Structural-RNN, we design a new deep neural architecture, named TRACK. TRACK achieves state-of-the-art performance on all considered datasets and prediction horizons, outperforming competitors by up to 20 percent on focus-type videos and horizons 2-5 seconds. The entire framework (codes and datasets) is online and received an ACM reproducibility badge https://gitlab.com/miguelfromeror/head-motion-prediction.

This companion paper is provided to describe the major experiments reported in our paper "On Learning Disentangled Representation for Acoustic Event Detection" published in ACM Multimedia 2019. To make the replication of our work easier, we first give an introduction of the computing environment where all of our experiments are conducted. Furthermore, we provide an environmental configuration file to setup the compiling environment and other artifacts including the source code, datasets and the files generated during our experiments. Finally, we summarize the structure and usage of the source code. For more details, please consult the README file in the archive of artifacts on GitHub: https://github.com/mastergofujs/SED_PyTorch.

Head motion prediction is an important problem with 360° videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article, we introduce a new deep architecture, named TRACK, that benefits both from the history of past positions and knowledge of the video content. We show that TRACK achieves state-of-the-art performance when compared against all recent approaches considering the same datasets and wider prediction horizons: from 0 to 5 seconds.

The streaming transmissions of 360° videos is a major challenge for the development of Virtual Reality, and require a reliable head motion predictor to identify which region of the sphere to send in high quality and save data rate. Different head motion predictors have been proposed recently. Some of these works have similar evaluation metrics or even share the same dataset, however, none of them compare with each other. In this article we introduce an open software that enables to evaluate heterogeneous head motion prediction methods on various common grounds. The goal is to ease the development of new head/eye motion prediction methods. We first propose an algorithm to create a uniform data structure from each of the datasets. We also provide the description of the algorithms used to compute the saliency maps either estimated from the raw video content or from the users' statistics. We exemplify how to run existing approaches on customizable settings, and finally present the targeted usage of our open framework: how to train and evaluate a new prediction method, and compare it with existing approaches and baselines in common settings. The entire material (code, datasets, neural network weights and documentation) is publicly available.

The development through streaming of 360\degree\ videos is persistently hindered by how much bandwidth they require. Adapting spatially the quality of the sphere to the user's Field of View (FoV) lowers the data rate but requires to keep the playback buffer small, to predict the user's motion or to make replacements to keep the buffered qualities up to date with the moving FoV, all three being uncertain and risky. We have previously shown that opportunistically regaining control on the FoV with active attention-driving techniques makes for additional levers to ease streaming and improve Quality of Experience (QoE). Deep neural networks have been recently shown to achieve best performance for video streaming adaptation and head motion prediction. This demo presents a step ahead in the important investigation of deep neural network approaches to obtain user-adaptive and network-adaptive 360 degree video streaming systems. In this demo, we show how snap-changes, an attention-driving technique, can be automatically modulated by the user's motion to improve the streaming QoE. The control of snap-changes is made with a deep neural network trained on head motion traces with the Deep Reinforcement Learning strategy A3C.

This artifact includes source code, scripts and datasets required to reproduce the experimental figures in the evaluation of the MM'18 paper, which is entitled "MiniView Layout for Bandwidth-Efficient 360-Degree Video". The artifact reports the comparison results among the standard cube layout (CUBE), the equi-angular layout (EAC), and the MiniView layout (MVL) in terms of compressed video size, visual quality of views and decoding and rendering time.

Despite exciting prospects, the development of 360° videos is persistently hindered by the difficulty to stream them. To reduce the data rate, existing streaming strategies adapt the video rate to the user's Field of View (FoV), but the difficulty of predicting the FoV and persistent lack of bandwidth are important obstacles to achieve best experience. In this article we exploit the recent findings on human attention in VR to introduce a new additional degree of freedom for the streaming algorithm to leverage: Virtuall Walls (VWs) are designed to translate bandwidth limitation into a new type of impairment allowing to preserve the visual quality by subtly limiting the user's freedom in well-chosen periods. We carry out experiments with 18 users and confirm that, if the VW is positioned after the exploration phase in scenes with concentrated saliency, a substantial fraction of users seldom perceive it. With a double-stimulus approach, we show that, compared with a reference with no VW consuming the same amount of data, VW can improve the quality of experience. Simulation of different FoV-based streaming adaptations with and without VW show that VW enables reduction in stalls and increases quality in FoV.

Cinematic Virtual Reality (VR) has the potential of touching the masses with new exciting experiences, but faces two main hurdles: one is the ability to stream these videos, another is their design and creation. Indeed, rates are much higher and in addition to discomfort and sickness that might arise in fully immersive experience with a headset, users might get lost when exploring a 360° videos and miss main elements required to understand the underlying plot. We take an innovative approach by addressing jointly the creation and streaming problems. We introduce a technique called snap-changes, aimed at directing viewers to points of interest pre-defined by the content producer. We design a VR editing tool and a custom 360° video player, to provide the content creator with the ability to drive the user's attention, and report results from two sets of user experiments that indicate that snap-changes indeed help reduce user's head motion.

A prominent portion of the traffic carried by Internet Service Providers (ISPs) is delivered by Content Delivery Networks (CDNs), the vast majority being video traffic served over HTTP adaptive streaming. CDNs often deploy their own caches at ISPs in addition to the servers deployed in their own premises. The ability of user devices to use two network interfaces simultaneously is instrumental in improving the quality of access, and this is enabled by multipath (MP) transport protocols such as MPTCP. This ability however precludes the use of caches in access networks. This article investigates the operational points obtained from the combination of MP routing with different cache locations. By modelling the optimization problems of both actors, ISPs and CDN, and designing a simulation testbed using a mobile operator topology, we identify for which metrics (video bitrate, acceptance rate, link congestion) and in which conditions (cache locations, multipath enabled or not, type of video content), the new operational points are interesting. Under realistic video patterns, multipath allows to improve served bitrates over all request types by up to 40% depending on the load.

The surge of video traffic is a challenge for service providers that need to maximize Quality of Experience (QoE) while optimizing the cost of their infrastructure. In this paper, we address the problem of routing multiple HTTP-based Adaptive Streaming (HAS) sessions to maximize QoE. We first design a QoS-QoE model incorporating different QoE metrics which is able to learn online network variations and predict their impact on representative classes of adaptation logic, video motion and client resolution. Different QoE metrics are then combined into a QoE score based on ITU-T Rec. P.1202.2. This rich score is used to formulate the routing problem. We show that, even with a piece-wise linear QoE function in the objective, the routing problem without controlled rate allocation is non-linear. We therefore express a routing-plus-rate allocation problem and make it scalable with a dual subgradient approach based on Lagrangian relaxation where subproblems select a single path for each request with a trivial search, thereby connecting explicitly QoE, QoE and HAS bitrate. We show with ns-3 simulations that our algorithm provides values for HAS QoE metrics (quality, rebufferings, variation) equivalent to MILP and better than QoS-based approaches.

International audience

While Virtual Reality (VR) represents a revolution in the user experience, current VR systems are flawed on different aspects. The difficulty to focus naturally in current headsets incurs visual discomfort and cognitive overload, while high-end headsets require tethered powerful hardware for scene synthesis. One of the major solutions envisioned to address these problems is foveated rendering. We consider the problem of streaming stored 360° videos to a VR headset equipped with eye-tracking and foveated rendering capabilities. Our end research goal is to make high-performing foveated streaming systems allowing the playback buffer to build up to absorb the network variations, which is permitted in none of the current proposals. We present our foveated streaming prototype based on the FOVE, one of the first commercially available headsets with an integrated eye-tracker. We build on the FOVE's Unity API to design a gaze-adaptive streaming system using one low- and one high-resolution segment from which the foveal region is cropped with per-frame filters. The low- and high-resolution frames are then merged at the client to approach the natural focusing process.

Video traffic constitutes the majority of traffic in bytes that mobile and fixed line operators deliver to their customer. This type of traffic is both resource consuming and QoE sensitive. Either because of content quality or QoE, a large fraction of users often abandon viewing prematurely. These abandonment phenomena lead to a huge waste of network resources and device batteries. Several strategies have been devised to account for all those dimensions. Dominant approaches are fast-caching where the server pushes traffic as fast as possible to the client in order to limit starvation, and ON-OFF strategy where the client forces the server to pause the transfer regularly in order to mitigate the wasted bytes and energy due to users abandonment. In this work, we focus on fast-caching and on ON-OFF strategies. We develop an analytical model which takes into account users dynamics and allows us to quantify the loss in bytes due to users' viewing abandonment. Furthermore, we formulate a multi-objective optimization problem to find ON and OFF period durations that feature a good trade-off between loss due to abandonment and starvation probability.

We consider delay tolerant mobile social networks (DTMSNs), which are opportunistic networks made of human-carried wireless devices clustered into social communities. In such environments, routing is a challenge as the limited resources (such as memory and contact opportunities) must be efficiently used and shared between the sessions (or users, contents). To handle several unicast sessions, inter-session network coding (ISNC) has been proved necessary for optimal throughput in general networks, but is a delicate problem as it can quickly get detrimental. This paper investigates that ISNC can be beneficial to DTMSNs when used on top of a social-aware routing algorithm, whereas we exemplify and make explicit why any gain can hardly be expected with greedy replication, in regard to the current literature on ISNC. We then design decentralized criteria to control when and where in the network ISNC should be triggered, based on the node features (buffer size and social relationships) and network current load. These criteria are tested extensively on real-world contact traces, in terms of various metrics, such as number of deliveries, mean delay, or fairness. Our online ISNC protocol builds on the SimBet utility-routing policy. Our ISNC protocol can, however, run on top of any social-aware routing.

Delay Tolerant Mobile Social Networks (DTMSNs) are networks made of human-carried wireless devices with intermittent connections, and whose physical meeting patterns make cluster into social communities. In such environments, routing is a challenge as the limited resources (like memory and contact opportunities) must be efficiently used and shared between the sessions (or users, contents). To handle several unicast sessions, Inter-Session Network Coding (ISNC) has been proven necessary for optimal throughput in general networks, but is a delicate problem as it can quickly get detrimental. This paper investigates empirically whether ISNC can be beneficial in DTMSNs. We first show that on a simple chain topology, without or with a hub node, no gain can be generally obtained when contacts are bidirectional. We then show that if non-directionality impedes ISNC gain, it can be due to greedy replication, and on the same DTMSN operated with a social-aware routing algorithm, the set of chosen routes turns ISNC into beneficial. On a butterfly topology, we investigate the impact, on ISNC gain, of key network parameters such as buffer management, copy (memory) budget and network load. This allows to determine what parameters to take into account when designing a decentralized ISNC criterion for general topologies.

Delay (or disruption) Tolerant Networks (DTNs) are networks made of wireless nodes with intermittent connections. In such networks, various opportunistic routing algorithms have been devised so as to cope with the lack of contemporaneous end-to-end route between a source and a destination. We consider DTNs made of mobile nodes clustered into social communities, with unicast sessions. Network coding is a generalization of routing that has been shown to bring a number of advantages in various communication settings. In particular, inter-session network coding (IS-NC) is known as a difficult optimization problem in general. In this article, we introduce a parameterized pairwise IS-NC control policy for heterogeneous DTNs, that encompasses both routing and coding controls with an energy constraint. We derive its performance modeling thanks to a mean-field approximation leading to a fluid model of the dissemination process, and validate the model with numerical experiments. We discuss the optimization problem of IS-NC control in social DTNs. By showing numerical gains, we illustrate the relevance of our approach that consists in designing IS-NC control policies not reasoning on specific nodes but instead on the coarse-grained underlying community structure of the social network.

This paper tackles the issue of reliable transport in delay-tolerant mobile ad hoc networks (DTNs) that are operated by some opportunistic routing algorithm. We propose a reliable transport mechanism that relies on acknowledgements (ACKs) and coding at the source. The various versions of the problem depending on buffer management policies are formulated and a fluid model based on mean-field approximation is derived for the designed reliable transport mechanism. This model allows both the mean file completion time and the energy consumption to be expressed up to the delivery of the last ACK at the source. The accuracy of this model is assessed through numerical simulations and a detailed investigation of the impact of the system parameters on the performance is conducted. We eventually present a joint optimization of the mean completion delay with or without an energy constraint, to identify the optimal set of parameters to use.

In Delay Tolerant Networks (DTN), contemporaneous end-to-end paths are rarely available. Routing in such networks is therefore one of the challenging issues. When the DTN is made of humans, human mobility characterizes the forwarding opportunities. To leverage the diversity of the strengths of social ties, a number of utility-based routing policies have been proposed. In this paper we first address theoretically the optimization problem of the routing policy in such a social DTN, under a multi-community network model, and we prove that the optimal policies have a per-community threshold structure, thereby generalizing the existing works for homogeneous mobility DTN. We then provide analysis of this result on a numerical example, and discuss the comparison of such optimal policies with the online utility-based policies of the literature.

In Delay Tolerant Networks (DTNs) the core challenge is to cope with lack of persistent connectivity and yet be able to deliver messages from source to destination. In particular, routing schemes that leverage relays' memory and mobility are a customary solution in order to improve message delivery delay. When large files need to be transferred from source to destination, not all packets may be available at the source prior to the first transmission. This motivates us to study general packet arrivals at the source, derive performance analysis of replication-based routing policies and study their optimization under two-hop routing. In particular, we determine the conditions for optimality in terms of probability of successful delivery and mean delay and we devise optimal policies, so-called it piecewise-threshold policies. We account for linear block-codes and rateless random linear coding to efficiently generate redundancy, as well as for an energy constraint in the optimization. We numerically assess the higher efficiency of piecewise-threshold policies compared with other policies by developing heuristic optimization of the thresholds for all flavors of coding considered.

We propose a new scheme for reliable transport, both for unicast and multicast flows, in Delay Tolerant Networks (DTNs). Reliability is ensured through the use of Global Selective ACKnowledgements (G-SACKs) which contain detailed (and potentially global) information about the receipt of packets at all the destinations. The motivation for using G-SACKs comes from the observation that one should take the maximum advantage of the contact opportunities which occur quite infrequently in DTNs. We also propose sharing of “packet header space” with G-SACK information and allow for random linear coding at the relay nodes. Our results from extensive simulations of the proposed scheme quantify the gains due to each new feature.

We study replication mechanisms that include Reed-Solomon type codes as well as network coding in order to improve the probability of successful delivery within a given time limit. We propose an analytical approach to compute these and study the effect of coding on the performance of the network while optimizing parameters that govern routing.

In this paper, a new class of low-density parity-check (LDPC) codes, named hybrid LDPC codes, is introduced. Hybrid LDPC codes are characterized by an irregular connectivity profile and heterogeneous orders of the symbols in the codeword. It is shown in particular that the class of hybrid LDPC codes can be asymptotically characterized and optimized using density evolution (DE) framework, and a technique to maximize the minimum distance of the code is presented. Numerical assessment of hybrid LDPC code performances is provided, by comparing them to protograph-based and multiedge-type (MET) LDPC codes. Hybrid LDPC codes are shown to allow to achieve an interesting tradeoff between good error-floor performance and good waterfall region with nonbinary coding techniques.

A class of two-bit message passing decoders for decoding column-weight-four LDPC codes over the binary symmetric channel is proposed. The thresholds for various decoders in this class are derived using density evolution. For a specific decoder, the sufficient conditions for correcting all error patterns with up to three errors are derived.

This paper is eligible for the student paper award. In this paper, we analyse asymptotically a new class of LDPC codes called non-binary hybrid LDPC codes, which has been recently introduced in L. Sassatelli and D. Declerq [2006]. We use density evolution techniques to derive a stability condition for hybrid LDPC codes, and prove their threshold behavior. We study this stability condition to conclude on asymptotic advantages of hybrid LDPC codes compared to their non-hybrid counterparts.