nils-herrmann/clean_scirepeval huggingface dataset:
Joint Network and Rate Allocation for Video Streaming over Multiple Wireless Networks

We address the problem of video streaming over multiple parallel networks. In the context of multiple users, accessing different types of applications, we are looking for efficient ways of allocating network resources and selecting network paths for each application, in order to maximize the overall systems performance. Our optimization joint problem consists of finding the appropriate application rate allocation and network parameters for each individual user, such that a universal system quality metric is maximized. A specific mapping between the requirements of each considered application and the overall quality metric is introduced, and our results are compared to other solutions based on throughput optimization strategies. The superiority and robustness of our approach is shown through extensive simulations in constant and dynamic systems, when clients can join/leave the access networks. Furthermore, we introduce heuristic algorithms which can obtain good results and are inexpensive in terms of computation and execution time.
Plenoptic spherical sampling

We present a novel plenoptic sampling scheme that permits an efficient representation of the full light ray field in a space limited by a convex closed surface. We show that a convenient way to sample the light ray field around an observer consists in using a discrete set of perspective imagers with overlapping field-of-views that are distributed on a closed convex surface and looking along the normal to the surface. Taking inspiration from the vision system of flying insects, we choose to constrain the cameras on a sphere of finite radius. Building on spectral analysis we propose a sampling scheme that permits to reconstruct the spherical light field without aliasing. We validate our framework through experiments in a synthetic environment and we show that our constructive sampling scheme permits to effectively reconstruct the light field without artifacts.
A regularization framework for mobile social network analysis

Mobile phone data provides rich dynamic information on human activities in social network analysis. In this paper, we represent data from two different modalities as a graph and functions defined on the vertex set of the graph. We propose a regularization framework for the joint utilization of these two modalities of data, which enables us to model evolution of social network information and efficiently classify relationships among mobile phone users. Simulations based on real world data demonstrate the potential application of our model in dynamic scenarios, and present competitive results to baseline methods for combining multimodal data in the learning and clustering communities.
Sparse FEC codes for flexible media protection

In this paper, we study block codes that are optimized to recover some lost source data even in case when full recovery is not possible. Conventionally, block codes designed for packet erasure networks are aimed to recover all the lost source packets, assuming that the amount of lost data does not exceed the redundancy overhead. Unfortunately, this approach leads to poor performance if the fraction of lost data even occasionally exceeds the limit for full recovery capability. Recovery of part of the data may prove to be beneficial, especially when media data packets are unequal in importance. We present a short linear block code design that improves the performance of traditional minimum distance separable (MDS) codes by reducing the fluctuation of the residual packet loss rate. These new codes also lead to a flexible design for unequal error protection of the media packets.
Linear manifold approximation based on differences of tangents

In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represents manifold data accurately while preserving the manifold's structure. For this purpose, we employ a greedy technique that partitions manifold samples into groups that can be well approximated by low dimensional subspaces. We start with considering each manifold sample as a different group and we use the difference of tangents to determine advantageous group mergings. We repeat this procedure until we reach the desired number of significant groups. At the end, the best low dimensional affine subspaces corresponding to the final groups constitute the manifold representation. Our experiments verify the effectiveness of the proposed scheme and show its superior performance compared to state-of-the-art methods for manifold approximation.
An overview of network coding for multimedia streaming

The objective of this paper is to survey recent developments of network coding, with specific focus on multimedia streaming. Network coding allows nodes to create and forward “combinations” of incoming messages, which has been shown to increase throughput. While network coding has been invented in the information theory field, its potential benefits are spurring new research on its multimedia applications. We first review the concept of network coding, and briefly describe its potential benefits from a multimedia communication perspective. Then, we discuss the specific issues imposed by media delay constraints on network coding algorithms. Finally, we review some recent works that develop network coding principles in media streaming applications.
Low complexity iterative multimedia resource allocation based on game theoretic approach

Efficient resource management strategies are important for multiuser multimedia applications, as they are often serviced over resource-constrained and shared network infrastructure. Moreover, an acceptable level of quality e.g., Quality of Service (QoS) should be guaranteed. In this paper, we consider a game-theoretic resource management strategy, where the bargaining solutions are deployed in the resource allocation. We are in particular interested in the Nash Bargaining Solution (NBS) that can allocate resources in a fair and optimal way, while explicitly considering the achieved utility. Finding the NBS, however, is a challenging task due to its potentially high computational complexity, especially when a large number of users and the large amount of resources are available. In order to overcome the problem, we propose an iterative approach that requires significantly lower computational complexity compared to the conventional approach. The proposed approach decomposes the bargaining problem into sub-bargaining problems, where a sub-bargaining problem considers smaller feasible set and computes the corresponding sub-NBS. This step is iteratively repeated for successive sub-bargaining problems until the NBS is obtained. We show that the proposed sub-NBS approaches the NBS with a small error while significantly reducing the complexity required to find the NBS.
Reinforcement learning-based opportunistic routing for live video streaming over multi-hop wireless networks

Real-time video services are usually delay sensitive and have strict constraints on the transmission reliability, which poses challenges to live video streaming over multi-hop wireless networks, since the unpredictable packet losses and network congestions caused by time-varying wireless channels greatly degrade the received video quality. To address this, in this paper, we propose a reinforcement learning (RL)-based opportunistic routing (OR) scheme for wireless video streaming with high-reliability and low-delay requirements. It can exploit the broadcast nature of the wireless shared medium and path diversity through OR to improve the transmission reliability, and find the low-delay paths between the source-destination pair dynamically for video packets through the RL module embedded in each relay node. Specifically, we design for the OR a new path-cost metric called the expected anypath delay (EAD), to estimate the end-to-end delay of a packet between the current relay node and the destination. The EAD is dynamically measured and updated over time, thereby reflecting the changes of link quality and the congestion level at the relay node. Moreover, we utilize the ACK message to piggyback the EAD of each relay node to its previous-hop node. Based on the local communication of the EADs from the neighbors, each node in the network can iteratively and independently run the RL module to update its own EAD value. Then, the next-hop forwarder node on a low delay route can be determined by assigning higher relay priority to the candidate forwarder nodes with lower EADs in OR. Simulation results show that the proposed RLOR algorithm can achieve a proper tradeoff between the transmission reliability and latency, so as to support the low-delay transmission of wireless video streams with high received video quality.
Joint reconstruction of compressed multi-view images

This paper proposes a distributed representation algorithm for multi-view images that are jointly reconstructed at the decoder. Compressed versions of each image are first obtained independently with random projections. The multiple images are then jointly reconstructed by the decoder, under the assumption that the correlation between images can be represented by local geometric transformations. We build on the compressed sensing framework and formulate the joint reconstruction as a l2-l1 optimization problem. It tends to minimize the MSE distortion of the decoded images, under the constraint that these images have sparse and correlated representations over a structured dictionary of atoms. Simulation results with multi-view images demonstrate that our approach achieves better reconstruction results than independent decoding. Moreover, we show the advantage of structured dictionaries for capturing the geometrical correlation between multi-view images.
Graph-based classification for multiple observations of transformed patterns

We consider the problem of classification when multiple observations of a pattern are available, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all the unlabelled samples belong to the same unknown class. We build on graph-based methods for semi-supervised learning and we optimize the graph construction in order to exploit the special structure of the problem. In particular, we assume that the optimal adjacency matrix is a linear combination of all possible class-conditional ideal adjacency matrices. We formulate the construction of the optimal adjacency matrix as a linear program (LP) on the weights of the linear combination. We provide experimental results that show the effectiveness and the validity of the proposed methodology.
Redundant image representations in security applications

To be efficient, data protection algorithms should generally exploit the properties of the media information in the transform domain. In this paper, we will advocate the use of nonlinear image approximations using highly redundant dictionaries, for security algorithms. We show that a flexible image representation based on a multidimensional and geometry-based coding scheme, has precious attributes for security information embedding. Redundant expansions provide very good approximation properties, as well as an increased resiliency to coding noise and a simple stream structure enables easy manipulations. This paper describes simple examples of image scrambling and watermarking applications, based on a matching pursuit image coder. It illustrates the very interesting potential of redundant decompositions for data protection and security applications.
Plenoptic based super-resolution for omnidirectional image sequences

This paper addresses the reconstruction of high resolution omnidirectional images from a low resolution video acquired by an omnidirectional camera moving in a static scene. In order to exploit the additional information provided by the side images in the video sequence, the ego-motion of the camera must be accurately estimated in a first step. The reconstruction can then be modeled as a plenoptic sampling problem that has to encompass the change of viewpoint between each position of the omnidirectional sensor and the specific discretization of the real scene observed from each position. We formulate this problem as an ill-posed inverse problem that incorporates a regularization term based on a Total Variation (TV) prior. A graph variational formulation is used in order to ease the representation of omnidirectional data and to adapt the discretization of differential operators to the omnidirectional geometry. Experimental results on synthetic images demonstrate the relevance of this approach and its superiority compared to standard super-resolution using a single image.
Multi-View Video Packet Scheduling

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time.
Distributed rate allocation in P2P networks with inter-session network coding

In this paper, we propose a distributed rate allocation algorithm for delay minimal data delivery in overlay networks where multiple sources compete simultaneously for the available network resources. In order to efficiently utilize the network resources, we propose to use inter-session network coding. We devise a distributed algorithm that allows peers to determine the optimal coding combinations and the packet rates for each type of combinations to be requested from the parent peers. The rate allocation problem is formulated as a decoding delay minimization problem, where every peer seeks the rates that minimize the average expected delay of the peer and its children peers. To solve this non-convex optimization problem, we introduce the concept of equivalent packet flows, which permits to estimate the expected number of packets that every peer needs to collect for decoding. We then decompose our original rate allocation problem into a set of convex subproblems, which we eventually combine to obtain the solution to the delay minimization problem. The results demonstrate that the proposed scheme eliminates effectively the bottlenecks and reduces the delay time experienced by users with limited resources.
Adaptive video streaming in lossy networks: versions or layers?

This work tackles low delay adaptive video streaming over error-prone networks. Our framework consists of an encoding station, an edge server and a set of clients with various access rates. The edge server is capable of performing simple error concealment operations on the incoming data before forwarding the adapted media to its clients. We study two encoding scenarios: versions (multiple encodings at various output rates) and layers. We develop a unified end-to-end distortion model, which we use to derive the optimal coding strategy for both scenarios. Finally we analyze the performance of MPEG-4 coded versions against MPEG-4 FGS-coded layers in rate-constrained lossy environments. Experiments show that versions perform better than layers when the constraint on the aggregate rate is somewhat relaxed, for low to medium packet loss ratios.
Correlation-aware packet scheduling for multi-camera streaming

In multi-view applications, multiple cameras acquire the same scene from different perspectives, which results in correlated video streams. It becomes important to exploit this correlation at the acquisition side (i.e., in the source coding) or at the receiver side (i.e., during error-concealment). In this work, we propose a correlation-aware scheduling algorithm for multi-camera sets, in which information from all views need to be sent over a bottleneck channel to clients that decode the 3D scene captured by the cameras. Based on a novel rate-distortion model, that takes into account the correlation between sources, we propose a solution that minimizes the distortion in the scene reconstruction and adapts to temporal variations in the scene content. Simulation results show the gain of the scheduling algorithm when the correlation model is known in the optimization, compared to scheduling policies with no information about the correlation or with a priori camera selection algorithms.
A Poisson Hidden Markov Model for Multiview Video Traffic

Multiview video has recently emerged as a means to improve user experience in novel multimedia services. We propose a new stochastic model to characterize the traffic generated by a Multiview Video Coding (MVC) variable bit-rate source. To this aim, we resort to a Poisson hidden Markov model (P-HMM), in which the first (hidden) layer represents the evolution of the video activity and the second layer represents the frame sizes of the multiple encoded views. We propose a method for estimating the model parameters in long MVC sequences. We then present extensive numerical simulations assessing the model's ability to produce traffic with realistic characteristics for a general class of MVC sequences. We then extend our framework to network applications where we show that our model is able to accurately describe the sender and receiver buffers behavior in MVC transmission. Finally, we derive a model of user behavior for interactive view selection, which, in conjunction with our traffic model, is able to accurately predict actual network load in interactive multiview services.
Improved Utility-Based Congestion Control for Delay-Constrained Communication

Due to the presence of buffers in the inner network nodes, each congestion event leads to buffer queueing and thus to an increasing end-to-end delay. In the case of delay sensitive applications, a large delay might not be acceptable and a solution to properly manage congestion events while maintaining a low end-to-end delay is required. Delay-based congestion algorithms are a viable solution as they target to limit the experienced end-to-end delay. Unfortunately, they do not perform well when sharing the bandwidth with congestion control algorithms not regulated by delay constraints (e.g., loss-based algorithms). Our target is to fill this gap, proposing a novel congestion control algorithm for delay-constrained communication over best effort packet switched networks. The proposed algorithm is able to maintain a bounded queueing delay when competing with other delay-based flows, and avoid starvation when competing with loss-based flows. We adopt the well-known price-based distributed mechanism as congestion control, but: 1) we introduce a novel non-linear mapping between the experienced delay and the price function and 2) we combine both delay and loss information into a single price term based on packet interarrival measurements. We then provide a stability analysis for our novel algorithm and we show its performance in the simulation results carried out in the NS3 framework. Simulation results demonstrate that the proposed algorithm is able to: achieve good intra-protocol fairness properties, control efficiently the end-to-end delay, and finally, protect the flow from starvation when other flows cause the queuing delay to grow excessively.
Dimensionality Reduction with Adaptive Approximation

In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using redundant dictionaries. A redundant dictionary is an overcomplete set of basis vectors that spans the signal space. The signals are jointly represented in a common subspace extracted from the redundant dictionary, using greedy pursuit algorithms for simultaneous sparse approximation. The design of the dictionary is flexible and enables the direct control on the shape and properties of the basis functions. Moreover, it allows to incorporate a priori and application-driven knowledge into the basis vectors, during the learning process. We apply our dimensionality reduction method to images and compare it with principal component analysis (PCA) and non-negative matrix factorization (NMF) and its variants, in the context of handwritten digit image recognition and face recognition. The experimental results suggest that the proposed dimensionality reduction algorithm is competitive to PCA and NMF and that it results into meaningful features with high discriminant value.
Media Aware Routing in Large Scale Networks with Overlay

This paper presents a new routing strategy, that selects the best network paths in an overlay network, in order to minimize the distortion perceived by the end user. We first propose a model that reports the video distortion as a function of the encoding rate, and the loss process parameters (the packet loss ratio, and the average burst length of errors). We then derive a method to compute an accurate estimation of the end-to-end characteristics of a given path in the network topology, as experienced by the media stream. It allows for reducing a set of streaming paths between the server and the client, to a simple virtual link with equivalent end-to-end parameters. Finally, an algorithm is proposed, that finds the best streaming paths in the overlay network, in terms of media distortion. Our algorithm therefore takes into account not only the conventional network parameters (e.g. end-to-end available bandwidth), but also other application specific metrics (e.g. video distortion measure and loss process). Interestingly, it is shown that the best route in terms of video distortion, does not necessarily use the highest bandwidth links, but carefully trades off channel reliability and bandwidth
Re-sampling and interpolation of DIBR-synthesized images using graph-signal smoothness prior

In depth-image-based rendering (DIBR), a new virtual viewpoint image is synthesized by mapping color pixels from one or more reference views to the new image grid using corresponding disparity values. However, due to necessary roundings to the 2D grid, "rounding holes" appear in synthesized objects, which are typically filled using local interpolation. In this paper, leveraging on a recent 3D image compression scheme called graph-based representation (GBR) that losslessly codes disparity information, we propose instead to re-sample and interpolate missing on-grid pixels of an object at decoder using mapped off-grid color pixels as reference. Specifically, we first describe the underlying data kernel for the desired signal using a weighted graph, where the edge weights reflect non-integer distances between reference and missing pixels. A graph-signal smoothness prior is then assumed to complete missing pixel values via iterative unconstrained quadratic optimization. Experimental results show that the synthesized objects have better quality than conventional local interpolation methods.