nils-herrmann/clean_scirepeval huggingface dataset:
Interleaved source coding (ISC) for predictive video coded frames over the Internet

Unreliable network's packet losses severely impact the playback quality of many predictive coded sources such as compressed video. Prior researches have shown various packet loss resilient coding methods to overcome such deficiency. In this paper, we propose a new packet-loss resilient coding method, interleaved source coding (ISC), based on an interleaving of predictive video coded frames transmitted over the Internet via a single erasure channel that is resilient to packet-losses. To select an interleaving pattern for a given erasure channel model, we employ a Markov decision process (MDP) and a corresponding dynamic programming algorithm. ISC improves the overall playback quality of predictive coded video frames over a lossy channel without complex modifications to standard predictive video coders. ISC eliminates the need for content distribution, path diversity routing, and related synchronization issues and hence, it provides a variable alternative to the path-diversity approaches. Simulations of a various video sequences showed that ISC significantly improves the playback quality of the predictive video over practical traces of Markov erasure channel model when compared with traditional non-interleaving predictive coding method.
On retransmission schemes for real-time streaming in the Internet

This paper presents a trace-driven simulation study of three classes of retransmission timeout (RTO) estimators in the context of low-bitrate real-time streaming over the Internet. We explore the viability of employing retransmission timeouts in NACK-based real-time streaming applications that support multiple retransmission attempts per lost packet. In such applications, real-time RTO estimation plays a major role (i.e., poor RTO estimation results in a larger number of duplicate packets and sometimes more frequent underflow events). Our study is based on trace data collected during a number of real-time streaming tests conducted between our dialup clients in all 50 states of the U.S. (including 653 major U.S. cities) and our backbone video server during a seven-month period. First, we define a generic performance measure for assessing the quality of hypothetical RTO estimators based on the samples of the round-trip delay (RTT) recorded in the trace data. Second, using this performance measure, we evaluate the class of TCP-like estimators, find the most optimal estimator given our performance measure, and establish power laws that describe the tradeoff between the optimal number of duplicate packets and the optimal timeout waiting time. Third, we introduce a new class of RTO estimators based on delay jitter and show that they perform significantly better than TCP-like estimators in NACK-based applications. Finally, we gain a major insight into the RTT process by establishing which tuning parameters of an RTO estimator make it optimal given our performance measure and our experimental data, and give our explanation of the observed phenomena.
Modeling, estimating and predicting the packet-level Bit Error Rate process in IEEE 802.15.4 LR-WPANs using Hidden Markov Models

This paper describes a stochastic wireless channel model that captures the behavior of the packet-level Bit Error Rate (BER) and the Link Quality Indication (LQI) processes. The model is based on a discrete-time Hidden Markov Model (HMM) whose hidden states correspond to different BERs, and whose observable states correspond to different LQI values. We use the Baum-Welch algorithm to train the HMM. The data required as input to the training phase is captured experimentally using IEEE 802.15.4 compliant Crossbow MICAz motes. We demonstrate the HMM-based Channel Model's (HCM) utility and versatility by three applications. In the first we use it to synthesize traces whose properties closely resemble those of the training data. This application simultaneously demonstrates the HCM's correctness as well. In the second application we demonstrate the HCM's ability to estimate a received packet's BER based on the LQI with which it was received. In the third application we demonstrate the HCM's ability to predict the BER to which future packets will be subjected. For evaluation purpose we restrict our prediction to the next packet.
Rate-Distortion Analysis and Quality Control in Scalable Internet Streaming

Rate-distortion (R-D) modeling of video coders has always been an important issue in video streaming; however, few of the traditional R-D models and their performance have been closely examined in the context of scalable (FGS-like) video. To overcome this shortcoming, the first half of the paper models rate-distortion of DCT-based fine-granular scalable coders and derives a simple operational R-D model for Internet streaming applications. Experimental results demonstrate that this R-D result, an extension of the classical R-D formula, is very accurate within the domain of scalable coding methods exemplified by MPEG-4 FGS and H.264 progressive FGS. In the second half of the paper, we examine congestion control and dynamic rate-scaling algorithms that achieve smooth visual quality during streaming using the proposed R-D model. In constant bitrate (CBR) channels, our R-D based quality-control algorithm dramatically reduces PSNR variation between adjacent frames (to less than 0.1 dB in sample sequences). Since the Internet is a changing environment shared by many sources, even R-D based quality control often cannot guarantee nonfluctuating PSNR in variable-bitrate (VBR) channels without the help from an appropriate congestion controller. Thus, we apply recent utility-based congestion control methods to our problem and show how a combination of this approach and our R-D model can benefit future streaming applications
Measurement Based Analysis and Modeling of the Error Process in IEEE 802.15.4 LR-WPANs

Knowledge of the error process and related channel parameters in wireless networks is invaluable and highly instrumental in a broad range of applications. Under the IEEE 802.15.4 Low Rate-Wireless Personal Area Networks (LR- WPAN) standard, compliant devices are capable of providing two pieces of information about the channel conditions along with each received packet, the Link Quality Indication (LQI) and Received Signal Strength Indication (RSSI). Together they constitute a form of Channel State Information (CSI). This work is based on statistical and information theoretic analysis of a very extensive data set of wireless channel traffic between a transmitter and receiver, called packet traces. Data is collected in a variety of documented environments. To our knowledge, this is the first detailed trace collection effort for this type of network. The traces distinguish themselves from data sets of other studies in that they record individual bit errors as well as packets that are never detected by receivers. First, we provide a detailed analysis of the IEEE 802.15.4 wireless channel. More specifically, we provide a detailed analysis of the Bit Error Rate (BER) process at individual bit and on a packet-by-packet basis. We explore the relationship between the packet-level BER process and the LQI and RSSI processes (also observable on a packet-by- packet). The analysis shows that measurements of both LQI and RSSI provide information that allows us to reduce uncertainty about the BER. Secondly, we develop a model of the BER process that is driven by observable CSI parameters. Thirdly, we continue our analysis with measurements of channel memory at the packet and bit level. We determine that the wireless channel 2 bits memory. At the packet level we observe that the amount of channel memory is more varied.
Performance evaluation: Priority transmission using network coding with multi-generation mixing

In this paper, we evaluate the priority transmission characteristic of Network Coding (NC) with Multi-Generation Mixing (MGM). MGM supports priority transmission by providing enhanced reliability for delivering different groups of sender packets. MGM is a generalized approach for practical network coding that enhances its performance. With MGM, sender packets are grouped in generations that constitute mixing sets. By employing the novel inter-generation network coding approach, each generation within a mixing set can be considered as a priority layer. This is due to the varying levels of data protection provided to the different generations within a mixing set. Traditionally, priority transmission is done by increasing the level of FEC protection assigned to sender data of higher priority. This incurs transmission overhead that consumes bandwidth. MGM supports priority transmission by network encoding data of higher priority in more sender packets so that more packets support its recovery. At the same time there is no increase in the total number of packets transmitted and hence no additional transmission overhead.
An optimal bit allocation framework for error resilient Scalable Video Coding

In this work, we propose an optimal joint bit allocation framework in order to provide unequal error protection (UEP) for scalable video coding. In the proposed scheme, video sequence is coded based on Scalable Video Coding (SVC) extension of the H.264/AVC standard. We generate different streams according to changing quantization parameters. Each stream divides into different sub-streams in terms of time and quality scalability. These sub-streams with different significance levels are channel-coded using Low Density Parity Check (LDPC) Codes with different coding rates respectively. Motivated by optimally allocating the bits to achieve resilience against channel induced corruptions, we propose an algorithm that optimally allocates the channel coding rates using a dynamic programming approach. We employ a probability model for decoding failure using LDPC codes. The results achieved clearly establish that the proposed framework can optimally allocate LDPC codes with varying coding rates to different sub-streams with the limited bandwidth.
Influence of Graph Properties of Peer-to-Peer Topologies on Video Streaming with Network Channel Coding

Network channel coding (NCC) distributes channel coding functions over network nodes participating in common or diverse communication sessions. A particular case of NCC is network embedded FEC (NEF), which has been shown to exhibit significant improvements in the performance of video streaming applications over multicast peer-to-peer (p2p) trees. The placement of NCC/NEF codecs and its utility in improving the throughput performance is in general a function of the underlying p2p graph topology. In this paper we consider two major forms of p2p topologies: (1) perfectly structured k-ary tree topologies that can be built from (virtually) ideal p2p graphs and (2) unstructured random tree topologies where new nodes randomly join as children to any of the existing peers. The two topologies represent an optimal low diameter structured p2p topology and a trivial randomly evolving sub-optimal topology, respectively. In this paper, we show the impact of key graph parameters, such as the maximum node-degree k and minimum tree-height D, on the performance of NCC in terms of NEF throughput as well as video quality for both structured and unstructured topologies. The utility of NCC/NEF for low-degree and/or less structured p2p topologies is especially highlighted by demonstrating that, embedding of additional codecs can render the performance of less structured topologies or higher diameter topologies to be almost as good as that of the very well structured low diameter topologies. We also investigate the impact of the graph properties on the placement of the NEF codecs
On low bit-rate coding using the contourlet transform

In this work, we study and analyze the contourlet transform for low bit-rate image coding. This image-based geometrical transform has been recently introduced to efficiently represent images with a spars set of coefficients. In order to explore the potentiality of this new transform as a tool for image coding, we developed a direct coding scheme that is based on using nonlinear approximation of images. We code the quantized transform coefficients as well as the significance map of an image in the contourlet transform domain. Based on the proposed approach, we analyzed the rate-distortion curves for a set of images and concluded that this coding approach, despite its redundancy, is visually competitive with a direct wavelet transform coder, and in particular, it is visually superior to wavelet coding for images with textures and oscillatory patterns.
Comparison of Multimedia Transport Schemes over Markovian Wireless Channels

Contemporary wireless networks drop all corrupted packets at the receivers. Such a strategy adversely affects multimedia applications which have stringent throughput and delay constraints. Recently, a header estimation scheme was proposed to reduce packet drops at wireless receivers , . These corrupted packets are recovered using FEC, which incurs lesser delay and bandwidth wastage than retransmissions. In this paper, we derive lower bounds on the excepted FEC redundancy required by ideal header estimation, UDP and UDP-Lite over an arbitrary-order Markov wireless channel. We show that under realistic wireless channel conditions, header estimation always requires lesser redundancy than UDP and UDP-Lite. We then propose a practical minimum distance decoding (MDD) header estimation scheme, which is receiver-based, low complexity and highly accurate. Trace-driven wireless video simulations demonstrate that MDD header estimation requires significantly lesser FEC redundancy and renders better video quality than existing schemes.
On Channel Capacity Estimation and Prediction for Rate-Adaptive Wireless Video

Packet drops caused by residue errors (MAC-layer errors) can severely deteriorate the wireless video quality. Prior studies have shown that this loss of quality can be circumvented by using forward error correction (FEC) to recover information from the corrupted packets. The performance of FEC encoded video streaming is critically dependent upon the choice of source and channel coding rates. In practice, the wireless channel conditions can vary significantly, thus altering the optimal rate choices. Thus, it is essential to develop an architecture which can estimate the channel capacity and utilize this estimate for rate allocation. In this paper we develop such a framework. Our contributions consist of two parts. In the first part we develop a prediction framework that leverages the received packets' signal to silence ratio (SSR) indications and MAC-layer checksum as side information to predict the operational channel capacity. In the second part, we use this prediction framework for rate allocation. The optimal rate allocation is dependent upon the channel capacity, the distribution of the (capacity) prediction error and the rate-distortion (RD) characteristics of the video source. Consequently, we propose a framework that utilizes the aforementioned statistics for RD optimal rate adaptation. We exhibit the efficacy of the proposed scheme by simulations using actual 802.11b wireless traces, an RD model for the video source and an ideal FEC model. Simulations using source RD models derived from five different popular video codecs (including H.264), show that the proposed framework provides up-to 5-dB improvements in peak signal-to-noise ratio (PSNR) when compared with conventional rate-adaptive schemes.
Sparse Expander-like Real-valued Projection (SERP) matrices for compressed sensing

Sparse binary projection matrices are arguably the most commonly used sensing matrices in combinatorial approaches to Compressed Sensing (CS). In this paper, we are interested in properties of Sparse Expander-like Real-valued Projection (SERP) matrices that are constructed by replacing the non-zero entries of sparse binary projection matrices by Gaussian random variables. We prove that these sparse real-valued matrices have a “weak” form of Restricted Isometery Property (RIP). We show that such weak RIP enables this class of matrices to be utilized in all three approaches to the problem of Compressed Sensing, i.e. greedy, geometrical and combinatorial.
A hybrid wavelet framework for modeling VBR video traffic

Traffic models play an important role in network simulation and performance analysis. This paper presents a frame-level hybrid framework for modeling variable bitrate (VBR) video traffic. To accurately capture long-range dependent (LRD) and short-range dependent (SRD) properties of video traffic, we incorporate elements of wavelet-domain analysis into classical time-domain modeling found in prior work. However, unlike previous studies, we analyze and successfully model both inter-GOP and intra-GOP correlation. Through the use of QQ plots and leaky-bucket simulations, we evaluate the accuracy of our approach and demonstrate that the autocorrelation function and the frame-size distribution of synthetic traffic match those of the original traffic very well. The leaky-bucket simulation also demonstrates that our model effectively preserves the temporal burstiness of the original video and can be used to predict buffer overflow probabilities and network packet loss.
Joint Estimation of Dictionary and Image from Compressive Samples

Effective compressed sensing of images relies on the prior knowledge of a well-suited dictionary for sparse representation of the target image. In the absence of such knowledge, which is a typical scenario in real-world applications, the following question arises: Would it be advantageous to take an off-the-shelf overcomplete dictionary and fine-tune the dictionary with respect to (the observed part of) the image? A primary obstacle in this approach is overfitting, i.e., the loss in model generalization to the whole image. In this paper, we establish that local dictionary optimization using the compressive samples reduces the image recovery error—relative to the off-the-shelf recovery—with an overwhelming probability that depends on the sampling matrix. We present joint estimation of dictionary and image (JEDI), an iterative algorithm for dictionary fine-tuning from compressive samples and analyze its performance for image recovery. Our algorithmic analysis is supplemented with numerical simulations under different random sampling patterns and off-the-shelf dictionary initializations.
CLIX: Network Coding and Cross Layer Information Exchange of Wireless Video

Network coding (NC) can be efficiently combined with the "physical layer broadcast" property of wireless mediums to facilitate mutual exchange of independent information. At the same time, experimental/theoretical analysis of wireless networks has shown the efficacy of cross-layer protocols that relay corrupted packets in bandwidth hungry video applications. The integration of NC-based information exchange and cross-layer (CL) protocols for wireless video is the primary theme of this work. A particular issue addressed in this paper is the impact of errors in a packet, on the performance of a network code. Thus, we identify the operating conditions under which NC, despite the presence of residue errors, is beneficial. Based on theoretical analysis and experiments using 802.11b wireless traces it is established that the combination of NC with relay of corrupted packets can perform better than (i) conventional schemes that drop all corrupted packets (ii) a scheme that deploys network coding only (iii) a scheme that only deploys a CL strategy that recovers information from corrupted packets. The proposed cross-layer exchange (CLIX) scheme significantly improves the performance of an H.264 based video codec for wireless networks.
Super-resolution for inconsistent scalable video streaming

We propose an example-based super-resolution method for a new framework of scalable video streaming. The proposed method is applicable to scalable video where the enhancement layer of some frames (e.g., within the same Group-of-Pictures) might be dropped due to changing network conditions. This leads to a streaming scenario that we call Inconsistent Scalable Video (ISV) streaming. In this paper, we focus on spatial ISV. In particular, at the decoder, the frames with the enhancement layer are used as the super-resolution dictionary for other video frames that their enhancement layers were dropped. Using the proposed method, the frames with dropped enhancement layer are scaled up to the original size. Our experimental results show a clear improvement over traditional interpolation based scaling.
Density and irregularity dependence of partial recovery codes

Classical linear block codes, such as Reed-Solomon (RS)-based codes, fail to recover any lost message symbols when the total losses exceed the redundant symbols. Under such adverse channel conditions, source and/or channel rate adaptation to incorporate additional redundancy might not be a viable option. In this paper, we explore a novel method of code adaptation which alters the degree distribution in order to achieve partial recovery of information when complete recovery is not possible. In particular, we change the degree distribution by adjusting the density and irregularity of the code. First, we illustrate that, while maintaining a constant rate, a partial recovery code can be optimized by density modification. Then, we focus on the Partial Reed-Solomon (PRS) codes, which are a family of RS-based codes that are capable of achieving different levels of partial recovery by adjustment to their order. We analyze the dependence of erasure recovery of these codes on density and regularity for a given number of losses. Finally, we present results and analysis which demonstrate that, for a given number of erasures, the PRS codes of order-1 render optimal (erasure-recovery) performance.
Multipath Distributed Data Reliability for Wireless Sensor Networks

In this work, we explore and evaluate data reliability in Wireless Sensor Networks (WSNs) using a novel approach that promises to increase the net network throughput with greater energy efficiency than prevalent schemes. Our results suggest that, under investigated scenarios, proposed approach can achieve reliable communication without fast depleting energy resources of individual sensor nodes. In particular, we propose a framework in which nodes collaborate, in a distributed fashion, to provide robustness against channel induced errors as data traverses the multi-hop network. Our scheme builds on two core concepts: partial decoding of incoming packets at intermediate nodes & directed diffusion of interest over the network for selective event based reliable path reinforcement. In addition, we use path diversity with selective budgeting of network energy resources to achieve data reliability. We show that the proposed scheme outperforms conventional error robustness approaches by considerable margin for a given network energy budget. We further discuss energy-distortion tradeoffs that may lead to greater network lifetime along-with maintaining a desired level of throughput. We use iteratively decodable LDPC codes to illustrate efficiency of our scheme.
Streaming fine-grained scalable video over packet-based networks

This paper presents an efficient method for streaming video that has the attribute of fine-granular scalability (FGS), which is a video coding method currently undergoing the MPEG-4 standardization process. An overview of FGS coding is given followed by a description of some issues associated with streaming the FGS base and enhancement layers over packet-based networks such as the Internet. The overall structure of the streaming system is described, and the detailed functionality of the algorithm is presented including simulation results, showing how to regulate the transmission of FGS video over networks that have packet loss or bandwidth variations.