Packet Loss Scenarios Research Articles

Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.

QoE-Based Performance Comparison of AVC, HEVC, and VP9 on Mobile Devices with Additional Influencing Factors

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users.

Cooperative Control of Multi-Agent Systems under Communication Delays and Packet Loss Scenarios

Cooperative Control of Multi-Agent Systems under Communication Delays and Packet Loss Scenarios

An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWhth in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities.

Accurate compressed traffic detection via traffic analysis using Graph Convolutional Network based on graph structure feature

As the application of data compression technology expands in areas such as IoT, webpage, and video data transmission, there are problems such as leakage of compressed but unencrypted user data, difficulty in supervising compressed data, and confusion between compressed and private encrypted traffic. Existing compressed traffic detection methods are significantly affected by data length and rely on binary classification in one step using random tests. It remains a challenging task to conduct accurate and efficient compressed traffic detection. In this paper, we present GCN-RTG, a compressed traffic detection method using Graph Convolutional Network. We investigate the randomness feature transformation pattern of packet sequences, propose a graph structure based on this pattern, and design a powerful GCN-based classifier to detect compressed traffic. The experimental results show that GCN-RTG achieves 94% accuracy in compressed traffic detection, remarkably improving by nearly 10% accuracy compared with traditional machine learning methods and approximately 5% compared with CNN and LSTM. Considering the effect of private encrypted traffic, GCN-RTG attains an accuracy of 89% for detecting compressed traffic. Furthermore, GCN-RTG can maintain an 83% accuracy even in the most extreme packet loss scenario and reach an outstanding accuracy of up to 95% of compressed traffic detection in real-world network data sized 4GB from the Jiangsu education backbone network in China.

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data.

Improved RTT Fairness of BBR Congestion Control Algorithm Based on Adaptive Congestion Window

To alleviate the lower performance of Transmission Control Protocol (TCP) congestion control over complex network, especially the high latency and packet loss scenario, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm. In contrast with other TCP congestion control algorithms, BBR adjusted transfer data by maximizing delivery rate and minimizing delay. However, some evaluation experiments have shown that the persistent queues formation and retransmissions in the bottleneck can lead to serious fairness issues between BBR flows with different round-trip times (RTTs). They pointed out that small RTT differences cause unfairness in the throughput of BBR flows and flows with longer RTT can obtain higher bandwidth when competing with the shorter RTT flows. In order to solve this fairness problem, an adaptive congestion window of BBR is proposed, which adjusts the congestion window gain of each BBR flow in network load. The proposed algorithms alleviate the RTT fairness issue by controlling the upper limit of congestion window according to the delivery rate and queue status. In the Network Simulator 3 (NS3) simulation experiment, it shows that the adaptive congestion window of BBR (BBR-ACW) congestion control algorithm improves the fairness by more than 50% and reduces the queuing delay by 54%, compared with that of the original BBR in different buffer sizes.

Assessment of QoE for Video and Audio in WebRTC Applications Using Full-Reference Models

WebRTC is a set of standard technologies that allows exchanging video and audio in real time on the Web. As with other media-related applications, the user-perceived audiovisual quality can be estimated using Quality of Experience (QoE) measurements. This paper analyses the behavior of different objective Full-Reference (FR) models for video and audio in WebRTC applications. FR models calculate the video and audio quality by comparing some original media reference with the degraded signal. To compute these models, we have created an open-source benchmark in which different types of reference media inputs are sent browser to browser while simulating different kinds of network conditions in terms of packet loss and jitter. Our benchmark provides recording capabilities of the impairment WebRTC streams. Then, we use different existing FR metrics for video (VMAF, VIFp, SSIM, MS-SSIM, PSNR, PSNR-HVS, and PSNR-HVS-M) and audio (PESQ, ViSQOL, and POLQA) recordings together with their references. Moreover, we use the same recordings to carry out a subjective analysis in which real users rate the video and audio quality using a Mean Opinion Score (MOS). Finally, we calculate the correlations between the objective and subjective results to find the objective models that better correspond with the subjective outcome, which is considered the ground truth QoE. We find that some of the studied objective models, such as VMAF, VIFp, and POLQA, show a strong correlation with the subjective results in packet loss scenarios.

Optical Flow Prediction for Blind and Non-Blind Video Error Concealment Using Deep Neural Networks

A novel optical flow prediction model using an adaptable deep neural network architecture for blind and non-blind error concealment of videos degraded by transmission loss is presented. The two-stream network model is trained by separating the horizontal and vertical motion fields which are passed through two similar parallel pipelines that include traditional convolutional (Conv) and convolutional long short-term memory (ConvLSTM) layers. The ConvLSTM layers extract temporally correlated motion information while the Conv layers correlate motion spatially. The optical flows used as input to the two-pipeline prediction network are obtained through a flow generation network that can be easily interchanged, increasing the adaptability of the overall end-to-end architecture. The performance of the proposed model is evaluated using real-world packet loss scenarios. Standard video quality metrics are used to compare frames reconstructed using predicted optical flows with those reconstructed using “ground-truth” flows obtained directly from the generator.

RT-CaCC: A Reliable Transport With Cache-Aware Congestion Control Protocol in Wireless Sensor Networks

Reliability of data transport in wireless sensor networks is critical in the presence of high packet loss level either due to link contentions or buffer congestions. Mechanisms such as intermediate caching and congestion control can enhance the reliability performance of transport protocols in the event of packet loss. However, these two mechanisms are designed independently for most cache-based transport protocols. In this paper, we developed a new reliable transport protocol with a cache-aware congestion control mechanism called RT-CaCC. RT-CaCC utilizes cache management policies such as cache insertion, cache elimination, and cache size allocation to mitigate packet losses in the network while maximizing cache utilization and bandwidth allocation. The protocol was evaluated in network topologies with different packet loss scenarios. Results showed that RT-CaCC obtained an average of 15%–38% improvement gain compared with baseline protocols. In addition, RT-CaCC consistently outperformed other cache-based transport protocols like distributed TCP caching and end-to-end reliable and congestion aware transport layer protocol in terms of cache utilization, end-to-end delay, and fairness metrics. Finally, the comparison between simulation and analytical results showed that there is an almost perfect match with the difference between curve points never exceeding 14% of the analytical cost.

Sparse Recovery-Based Error Concealment

Image and video transmission over heterogeneous networks may encounter packet loss due to the channel impairments, leading to quality degradation of the received image. In this paper, a novel robust image transmission system is proposed by casting the error concealment challenge into a sparse recovery framework. To this purpose, a robust encoder is carefully designed in order to mitigate the negative effects of the packet loss. After wavelet decomposition, a quadtree structure of the wavelet coefficients is used to rearrange them into independent partitions. Random linear combinations of coefficients for each partition are then adopted to provide a high error recovery capability. This linear process coupled with a simple packetization process introduces more robustness and error resilience into the transmission system. At the receiver side, the tree-sparse structure of the wavelet coefficients is explicitly exploited in order to model the error recovery problem as a sparse recovery framework. This is achieved by adaptation of a well-known iterative sparse reconstruction algorithm to the defined tree structure built in the wavelet domain. Compared with the state-of-the-art error concealment algorithms, experimental results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations over a range of packet loss rates, and ensure that a high-quality image can be recovered for the high packet loss scenarios.

Digital Speech Interpolation Advantage of Statistical Time Division Multiplexer

This paper discusses Digital Speech Interpolation (DSI) advantage of Statistical Time Division Multiplexer (STDM) for Random Packet Loss scenarios. In order to harness this advantage each source speech is compressed at 6.4 kbps, Voice Activity Detector (VAD) is used for each source, and packet loss mechanism is introduced to achieve maximum DSI advantage. It is observed that for maximum of 9 users channel capacity, extra 3 users, i.e. total 12 users can be accommodated with 3% speech frame losses, which results in an advantage of 12/9 = 1.33.

Analysis of Queuing Delay and Medium Access Distribution over Wireless Multihop PANs

This paper proposes a novel analytical approach to the analysis of the queuing delay and medium access distribution of carrier sense multiple access with collision avoidance (CSMA-CA) protocols over multihop personal area networks (MPANs). The proposed approach considers a general case without restrictions on the topology, traffic generation patterns, or multipath routing policy, and it accounts for heterogeneous traffic patterns, all possible packet loss scenarios, and the effect of the length of the backoff on the channel assessment. This paper uses statistical theorems to derive the moment generating function (mgf) of the time required to service a packet and then uses this function as a service distribution in a GI/G/1 queuing model to derive the interdeparture and total queuing delays. From these functions, the end-to-end delay, jitter, throughput, and failure transmission probability over a path are derived. In addition to determining these metrics, this paper exploits the statistical features of the mgf to determine the rare-event probability of the service function. The proposed approach is exemplified using the state-of-the-art IEEE 802.15.4 CSMA-CA protocol, and its accuracy is demonstrated through comparison with simulations.

On the performance of video quality assessment metrics under different compression and packet loss scenarios.

When comparing the performance of video coding approaches, evaluating different commercial video encoders, or measuring the perceived video quality in a wireless environment, Rate/distortion analysis is commonly used, where distortion is usually measured in terms of PSNR values. However, PSNR does not always capture the distortion perceived by a human being. As a consequence, significant efforts have focused on defining an objective video quality metric that is able to assess quality in the same way as a human does. We perform a study of some available objective quality assessment metrics in order to evaluate their behavior in two different scenarios. First, we deal with video sequences compressed by different encoders at different bitrates in order to properly measure the video quality degradation associated with the encoding system. In addition, we evaluate the behavior of the quality metrics when measuring video distortions produced by packet losses in mobile ad hoc network scenarios with variable degrees of network congestion and node mobility. Our purpose is to determine if the analyzed metrics can replace the PSNR while comparing, designing, and evaluating video codec proposals, and, in particular, under video delivery scenarios characterized by bursty and frequent packet losses, such as wireless multihop environments.

HEVStream: a framework for streaming and evaluation of high efficiency video coding (HEVC) content in loss-prone networks

High Efficiency Video Coding (HEVC) is the next generation video compression standard currently under development within the ITU-T/ISO sponsored Joint Collaborative Team on Video Coding (JCT-VC). The standardization, and eventual adoption, of HEVC will contribute significantly to the future development of many consumer devices. Areas such as broadcast television, multimedia streaming, mobile communications and multimedia/video content storage will all be impacted by implementation of the emerging HEVC standard. Up to this point in time the research focus of HEVC has been on improvements to video compression efficiency and little work has been conducted into streaming of HEVC. In this work we consider the practical barriers to HEVC streaming in realistic environments and propose HEVStream, a streaming and evaluation framework for HEVC encoded content. Our framework fills the current gap in enabling networked HEVC visual applications and permits the implementation, testing and evaluation of HEVC encoded video streaming under a range of packet loss, bandwidth restriction and network delay scenarios in a realistic testbed environment. We provide a basic error concealment method for HEVC to overcome limitations within the decoder and an RTP packetisation format for HEVC Network Abstraction Layer (NAL) units. Comprehensive results of HEVC streaming experiments under various network circumstances are reported. These results provide an insight into the reduction in picture quality, measured as peak signal to noise ratio (PSNR), that can be expected under a wide range of network constraint and packet loss conditions. We report an average loss of 3.61dB when a bandwidth reduction of 10% is applied. We believe that this work will be amongst the first to report on successful design and implementation of HEVC network applications, and evaluation of the effects of network constraints or limitations on the quality of HEVC encoded video streams.

Efficient progressive downloading over multimedia broadcast multicast service

The paper introduces an efficient way of delivering multimedia files over the multimedia broadcast multicast service (MBMS) and study the performance of the proposed MBMS download system on initial waiting time for progressive downloads of streamable contents. Multimedia streaming or downloading over broadcast and multicast networks is recently become conceivable for mobile users. Now there is an increasing interest to deliver broadcast and multicast data over the third generation (3G) networks such as The universal mobile telecommunication system (UMTS) since “send once charge many times” approach makes this type of services very attractive for 3G operators. The third generation partnership project (3GPP) has recently introduced MBMS to support IP multicasting services in UMTS. Streaming and downloading are two main services considered over MBMS. A third type of service, namely progressive download inherits the advantages of using download mechanism such as efficient usage of radio resources while it also enables users to stream multimedia files with some initial waiting time. However, there is few works that study progressive download in MBMS and current version of MBMS release have not yet an efficient mechanism to support progressive downloading. This article proposes a new approach for MBMS download mechanism that supports progressive download. The proposed systems are tested under various link conditions and packet loss scenarios specific for MBMS. The results, compared with the recent works, which have the same test assumptions, show a dramatic decrease in initial waiting time of the progressive download.

Robust decoder-based error control strategy for recovery of H.264/AVC video content

Real-time wireless conversational and broadcasting multimedia applications offer particular transmission challenges as reliable content delivery cannot be guaranteed. The undelivered and erroneous content causes significant degradation in quality of experience. The H.264/AVC standard includes several error resilient tools to mitigate this effect on video quality. However, the methods implemented by the standard are based on a packet-loss scenario, where corrupted slices are dropped and the lost information concealed. Partially damaged slices still contain valuable information that can be used to enhance the quality of the recovered video. This study presents a novel error recovery solution that relies on a joint source-channel decoder to recover only feasible slices. A major advantage of this decoder-based strategy is that it grants additional robustness while keeping the same transmission data rate. Simulation results show that the proposed approach manages to completely recover 30.79% of the corrupted slices. This provides frame-by-frame peak signal-to-noise ratio (PSNR) gains of up to 18.1 dB, a result which, to the knowledge of the authors, is superior to all other joint source-channel decoding methods found in literature. Furthermore, this error resilient strategy can be combined with other error resilient tools adopted by the standard to enhance their performance.

Atherosclerotic Plaque Ultrasound Video Encoding, Wireless Transmission, and Quality Assessment Using H.264

We propose a unifying framework for efficient encoding, transmission, and quality assessment of atherosclerotic plaque ultrasound video. The approach is based on a spatially varying encoding scheme, where video-slice quantization parameters are varied as a function of diagnostic significance. Video slices are automatically set based on a segmentation algorithm. They are then encoded using a modified version of H.264/AVC flexible macroblock ordering (FMO) technique that allows variable quality slice encoding and redundant slices (RSs) for resilience over error-prone transmission channels. We evaluate our scheme on a representative collection of ten ultrasound videos of the carotid artery for packet loss rates up to 30%. Extensive simulations incorporating three FMO encoding methods, different quantization parameters, and different packet loss scenarios are investigated. Quality assessment is based on a new clinical rating system that provides independent evaluations of the different parts of the video (subjective). We also use objective video-quality assessment metrics and estimate their correlation to the clinical quality assessment of plaque type. We find that some objective quality assessment measures computed over the plaque video slices gave very good correlations to mean opinion scores (MOSs). Here, MOSs were computed using two medical experts. Experimental results show that the proposed method achieves enhanced performance in noisy environments, while at the same time achieving significant bandwidth demands reductions, providing transmission over 3G (and beyond) wireless networks.

Improved error concealment of region of interest based on the H.264/AVC standard

Video transmission over error-prone channels often suffers from inevitable transmission errors, which necessitates proper error concealment (EC) for acceptable image quality. Furthermore, the region of interest (ROI) in images usually draws much attention, and so the EC of the ROI receives special treatment during encoding and decoding. We explore a data hiding-based scheme to effectively improve the EC of the ROI in the case of erasures of large continuous regions, which becomes impractical for conventional EC methods. At the encoder side, motion vectors of the ROI are adaptively embedded in the background based on original quantized coefficients of background macroblocks. Considering the limited embedding capacity of the background, we further propose to assign priorities to each ROI macroblock based on a predefined metric of error propagation. Our scheme is applied with the state-of-the-art H.264/AVC standard in a packet loss scenario, and better video quality can be obtained. Experimental results show that the scheme can improve the EC of the ROI significantly without much loss of coding efficiency.

An Analytic Throughput Model for TCP NewReno

This paper develops a simple and accurate stochastic model for the steady-state throughput of a TCP NewReno bulk data transfer as a function of round-trip time and loss behavior. Our model builds upon extensive prior work on TCP Reno throughput models but differs from these prior works in three key aspects. First, our model introduces an analytical characterization of the TCP NewReno fast recovery algorithm. Second, our model incorporates an accurate formulation of NewReno's timeout behavior. Third, our model is formulated using a flexible two-parameter loss model that can better represent the diverse packet loss scenarios encountered by TCP on the Internet. We validated our model by conducting a large number of simulations using the ns-2 simulator and by conducting emulation and Internet experiments using a NewReno implementation in the BSD TCP/IP protocol stack. The main findings from the experiments are: 1) the proposed model accurately predicts the steady-state throughput for TCP NewReno bulk data transfers under a wide range of network conditions; 2) TCP NewReno significantly outperforms TCP Reno in many of the scenarios considered; and 3) using existing TCP Reno models to estimate TCP NewReno throughput may introduce significant errors.