Bandwidth Estimation Research Articles

Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

The 360-degree video streaming has higher bandwidth requirements compared with traditional video to achieve the same user-perceived playback quality. Since users only view part of the entire videos, viewport-adaptive streaming is an effective approach to guarantee video quality. However, the performance of viewport-adaptive schemes is highly dependent on the bandwidth estimation and viewport prediction. To overcome these issues, we propose a novel reinforcement learning (RL) based viewport-adaptive streaming framework called RLVA, which optimizes the 360-degree video streaming in viewport prediction, prefetch scheduling and rate adaptation. Firstly, RLVA adopts t location-scale distribution rather than Gaussian distribution to describe the viewport prediction error characteristic more accurately and achieve the tile viewing probability based on the distribution. Besides, a tile prefetch scheduling algorithm is proposed to update the tiles according to the latest prediction results, which further reduces the adverse effect of prediction error. Furthermore, the tile viewing probabilities are treated as input status of RL algorithm. In this way, RL can adjust its policy to adapt to both of the network conditions and viewport prediction error. Through extensive evaluations, the simulation results show that the proposed RLVA outperforms other viewport-adaptive methods by about 4.8%-66.8% improvement of Quality of Experience (QoE) and effectively reduces the impact of viewport prediction errors.

Access network selection based on available bandwidth estimation for heterogeneous networks

With the new 5G trend, the amount of heterogeneous networks (HetNets) has increased exponentially. Cooperation between the most commonly used HetNets such as: WiFi and Long Term Evolution (LTE) is required to ensure a balanced traffic load which benefits the end user with maximum throughput and high quality of service. A balanced traffic problem with WiFi- LTE heterogeneous wireless networks is selecting which network to use, LTE or WiFi, if both are available. In other words, a node would have to send/receive traffic using either LTE or WiFi. We address this issue and propose both a WiFi available bandwidth estimation technique called “Available Bandwidth with Error Probability (ABEP)” and a traffic steering strategy to improve the performance of WiFi-LTE wireless networks. Our approach provides a better estimation of available bandwidth in WiFi with respect to relevant bandwidth estimation techniques. The bandwidth utilization in a WiFi Network is obtained at an initial stage of the traffic steering strategy. The steering of traffic is then triggered depending on events and the network conditions. Different from other approaches, our proposed traffic steering algorithm considers both a minimum bandwidth and a minimum SNR threshold to connect to WiFi, which are necessary to have an efficient transmission. One of the benefits of our approach regards reducing the use of the LTE network and its corresponding monetary charges.

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud.

In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server’s utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

Spectral processing techniques for efficient monitoring in optical networks

Having ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB.

Distributed optimal congestion control and channel assignment in wireless mesh networks

Wireless mesh networks have numerous advantages in terms of connectivity as well as reliability. Traditionally the nodes in wireless mesh networks are equipped with single radio, but the limitations are lower throughput and limited use of the available wireless channel. In order to overcome this, the recent advances in wireless mesh networks are based on multi-channel multi-radio approach. Channel assignment is a technique that selects the best channel for a node or to the entire network just to increase the network capacity. To maximize the throughput and the capacity of the network, multiple channels with multiple radios were introduced in these networks. In the proposed system, algorithms are developed to improve throughput, minimise delay, reduce average energy consumption and increase the residual energy for multi radio multi-channel wireless mesh networks. In literature, the existing channel assignment algorithms fail to consider both interflow and intra flow interferences. The limitations are inaccurate bandwidth estimation, throughput degradation under heavy traffic and unwanted energy consumption during low traffic and increase in delay. In order to improve the performance of the network distributed optimal congestion control and channel assignment algorithm (DOCCA) is proposed. In this algorithm, if congestion is identified, the information is given to previous node. According to the congestion level, the node adjusts itself to minimise congestion.

Nonparametric density estimation and bandwidth selection with B-spline bases: A novel Galerkin method

A general and efficient nonparametric density estimation procedure for local bases, including B-splines, is proposed, which employs a novel statistical Galerkin method combined with basis duality theory. To select the bandwidth, an efficient cross-validation procedure is introduced, based on closed-form expressions in terms of the primal and dual B-spline basis. By utilizing a closed-form expression for the dual basis, the least-squares cross validation formula is calculated in closed-form, enabling an efficient estimation of the optimal bandwidth. The full computational procedure achieves optimal complexity, and is very accurate in comparison with existing estimation procedures, including state-of-the-art kernel density estimators. The presented theoretical results are supported by extensive numerical experiments, which demonstrate the efficiency and accuracy of the new methodology. This new approach provides a complete and optimally efficient framework for density estimation with a B-spline basis, based on simple and elegant closed-form estimators with theoretical convergence results that are substantiated in numerical experiments.

Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography.

The purpose of this work is to provide a theoretically grounded assessment on the field of view and bandwidth of a lensless holographic setup. Indeed, while previous works have presented results with super-resolution and field-of-view extrapolation, there are no well-established rules to determine them. We show that the theoretical field of view can be large with a spatial-frequency bandwidth only limited by the wavelength, leading to an unthinkable number of degrees of freedom. To keep a realistic field of view and bandwidth, we propose several practical bounds based on a few setup properties, namely, the noise level and spatiotemporal coherence of the source.

Online available bandwidth estimation using multiclass supervised learning techniques

In order to answer how much bandwidth is available to an application from one end to another in a network, state-of-the-art estimation techniques, based on active probing, inject artificial traffic with a known structure into the network. At the receiving end, the available bandwidth is estimated by measuring the structural changes in the injected traffic, which are caused by the network path. However, bandwidth estimation becomes difficult when packet distributions are distorted by non-fluid bursty cross traffic and multiple links. This eventually leads to an estimation bias. One known approach to reduce the bias in bandwidth estimations is to probe a network with constant-rate packet trains and measure the average structural changes in them. However, one cannot increase the number of packet trains in a designated time period as much as needed because high probing intensity overloads the network and results in packet losses in probe and cross traffic, which distorts probe packet gaps and inflicts more bias. In this work, we propose a machine learning-based, particularly classification-based, method that provides reliable estimates utilizing fewer packet trains. Then, we implement supervised learning techniques. Furthermore, considering the correlated changes over time in traffic in a network, we apply filtering techniques on estimation results in order to track the changes in the available bandwidth. We set up an experimental testbed using the Emulab software and a dumbbell topology in order to create training and testing data for performance analysis. Our results reveal that our proposed method identifies the available bandwidth significantly well in single-link networks as well as networks with heavy cross traffic burstiness and multiple links. It is also able to estimate the available bandwidth in randomly generated networks where the network capacity and the cross traffic intensity vary substantially. We also compare our technique with the others that use direct probing and regression approaches, and show that ours has better performance in terms of standard deviation around the actual bandwidth values.

Bayesian bandwidth estimation for local linear fitting in nonparametric regression models

Abstract This paper presents a Bayesian sampling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian sampling approach, the error density is approximated by a location-mixture density of Gaussian densities with means the individual errors and variance a constant parameter. This mixture density has the form of a kernel density estimator of errors and is referred to as the kernel-form error density (c.f. Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34.). While (Zhang, X., M. L. King, and H. L. Shang. 2014. “A Sampling Algorithm for Bandwidth Estimation in a Nonparametric Regression Model with a Flexible Error Density.” Computational Statistics & Data Analysis 78: 218–34) use the local constant (also known as the Nadaraya-Watson) estimator to estimate the regression function, we extend this to the local linear estimator, which produces more accurate estimation. The proposed investigation is motivated by the lack of data-driven methods for simultaneously choosing bandwidths in the local linear estimator of the regression function and kernel-form error density. Treating bandwidths as parameters, we derive an approximate (pseudo) likelihood and a posterior. A simulation study shows that the proposed bandwidth estimation outperforms the rule-of-thumb and cross-validation methods under the criterion of integrated squared errors. The proposed bandwidth estimation method is validated through a nonparametric regression model involving firm ownership concentration, and a model involving state-price density estimation.

Round-Trip Time and Available Bandwidth Estimation Based Congestion Window Reduction Algorithm of MPTCP in Lossy Satellite Networks

A significant enhancement in recent communication systems is Satellite Communications. The performance improvement of the transmission control protocol (TCP) in satellite communications has been an active research area. Multipath TCP (MPTCP) ensures a robust solution than the regular TCP. However, the performance degradation in satellite networks due to the link asymmetry, high loss and high latency cannot distinguish the packet losses caused by network congestions from the losses properly. In this paper, a round-trip time detection-based algorithm is proposed using available bandwidth estimation (ABE) to mitigate the issue of blind decreasing of congestion window upon detecting the packet loss over lossy satellite networks. Experiments are done using the NS-2 network simulator to compare the various congestion control algorithms of both TCP and MPTCP over satellite networks. Simulations results from the experiments show that our proposed algorithm outperforms the existing algorithms which are mentioned in this article.

Bayesian Bandwidth Test and Selection for High-dimensional Banded Precision Matrices

Assuming a banded structure is one of the common practice in the estimation of high-dimensional precision matrices. In this case, estimating the bandwidth of the precision matrix is a crucial initial step for subsequent analysis. Although there exist some consistent frequentist tests for the bandwidth parameter, bandwidth selection consistency for precision matrices has not been established in a Bayesian framework. In this paper, we propose a prior distribution tailored to the bandwidth estimation of high-dimensional precision matrices. The banded structure is imposed via the Cholesky factor from the modified Cholesky decomposition. We establish strong model selection consistency for the bandwidth as well as consistency of the Bayes factor. The convergence rates for Bayes factors under both the null and alternative hypotheses are derived which yield similar order of rates. As a by-product, we also propose an estimation procedure for the Cholesky factors yielding an almost optimal order of convergence rates. Two-sample bandwidth test is also considered, and it turns out that our method is able to consistently detect the equality of bandwidths between two precision matrices. The simulation study confirms that our method in general outperforms the existing frequentist and Bayesian methods.

Density deconvolution for generalized skew-symmetric distributions

The density deconvolution problem is considered for random variables assumed to belong to the generalized skew-symmetric (GSS) family of distributions. The approach is semiparametric in that the symmetric component of the GSS distribution is assumed known, and the skewing function capturing deviation from the symmetric component is estimated using a deconvolution kernel approach. This requires the specification of a bandwidth parameter. The mean integrated square error (MISE) of the GSS deconvolution estimator is derived, and two bandwidth estimation methods based on approximating the MISE are also proposed. A generalized method of moments approach is also developed for estimation of the underlying GSS location and scale parameters. Simulation study results are presented including a comparing the GSS approach to the nonparametric deconvolution estimator. For most simulation settings considered, the GSS estimator is seen to have performance superior to the nonparametric estimator.

Data-driven software reliability evaluation under incomplete knowledge on fault count distribution

In this article, we consider data-driven approaches for software reliability evaluation without specifying the fault count distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive non-parametric method based on the kernel estimation is provided with several kernel functions and bandwidth estimations, in addition to the non-parametric bootstrap. The resulting data-driven methodologies can give useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault count distribution.

Bayesian nonparametric estimation of bandwidth using mixtures of kernel estimators for length-biased data

ABSTRACTKernel density estimation has been applied in many computational subjects. In this paper, we propose a density estimation procedure from a Bayesian nonparametric perspective using Dirichlet process prior for the length-biased data under an unknown kernel function. In this situation, the kernel within the Dirichlet process mixture model will be approximated by the kernel density estimator. We present a Bayesian nonparametric method for finding the bandwidth parameter in the kernel density estimation using a Markov chain Monte Carlo approach. Then, this approach is used to the simulated and real data set. Finally, we compare the proposed bandwidth estimation with the other estimations like cross-validation and Bayes based on the mean integrated squared error criterion.

Research of Wireless Congestion Control Algorithm Based on EKF

The random variation of bandwidth in wireless networks causes some significant challenges to the congestion control protocols based on bandwidth estimation. In this paper, a wireless congestion control scheme based on extended Kalman filtering and bandwidth (CSEKB) is proposed. The CSEKB can effectively perceive the bandwidth oscillation of wireless networks and distinguish the type of packet loss by establishing a noise perception factor. According to the congestion factor, the congestion control parameters are adjusted to correspondingly improve the performance of the wireless network. Moreover, the variation trend of the size of the congestion window presents a law of similar normal distribution curve, which has a certain degree of local symmetry. The CSEKB was implemented in Network simulator 3 (NS3) and compared with TCP Westwood (TCPW), CUBIC, and extended Kalman filtering-based bandwidth estimation (EBE). Through extensive simulation studies, the proposed CSEKB demonstrated the significant performance in wireless networks. First, the CSEKB can achieve congestion control based on the accurate prediction of available bandwidth, and improve average throughput and link utilization. In addition, the CSEKB has good fairness and friendliness compared with several other well-known congestion control methods.

Underwater Wireless Information Transfer with Compressive Sensing for Energy Efficiency

Acquisition of sensor data in wireless sensor networks with compressive sensing reduces the cost involved with sensing and communication. The challenge behind this work in underwater environment is exploring the fact of transfer capability of sensor nodes and underwater channel. The robustness of compressive sensing scheme in underwater environment is further augmented by recovery and transfer path. Two algorithms have been proposed. The first which uses compressive sensing at source and reconstruct using orthogonal matching pursuit at sink named as Underwater Wireless Information Transfer with Compressive sensing. Second algorithm exploits bandwidth estimation exploiting the cross traffic at intermediate forwarders for each source sensors to its associated sink named as: Underwater Wireless Information Transfer with Compressive sensing Bandwidth Measurements. Performance metrics of both protocols are interpreted through simulations.

Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density

ABSTRACT This study examines the optimal selections of bandwidth and semi-metric for a functional partial linear model. Our proposed method begins by estimating the unknown error density using a kernel density estimator of residuals, where the regression function, consisting of parametric and nonparametric components, can be estimated by functional principal component and functional Nadayara-Watson estimators. The estimation accuracy of the regression function and error density crucially depends on the optimal estimations of bandwidth and semi-metric. A Bayesian method is utilized to simultaneously estimate the bandwidths in the regression function and kernel error density by minimizing the Kullback-Leibler divergence. For estimating the regression function and error density, a series of simulation studies demonstrate that the functional partial linear model gives improved estimation and forecast accuracies compared with the functional principal component regression and functional nonparametric regression. Using a spectroscopy dataset, the functional partial linear model yields better forecast accuracy than some commonly used functional regression models. As a by-product of the Bayesian method, a pointwise prediction interval can be obtained, and marginal likelihood can be used to select the optimal semi-metric.

Rethinking the rate estimation of BBR congestion control

The acknowledgment (ACK) compression makes the available bandwidth estimation in bottleneck bandwidth and round-trip propagation time (BBR) inaccurate which results in the high queuing delay under low packet loss rate environments. The additional queuing delay affects the user experience of interactive applications like Skype. To solve the above-mentioned issue, this Letter presents a novel method, named calibrating bandwidth estimation (CBE), to calibrate the bandwidth estimation. The CBE can detect the implausible ACK rates accurately and the extensive simulation prove the effectiveness of CBE.

A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data

Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.

Performance analysis on round trip available bandwidth estimation tools for wireless mesh network

In wireless mesh network environment, available bandwidth (ABW) estimation is important to provide feedback on the quality of network infrastructure to end user. Recent study shows the emerging of Round Trip (RT) based ABW tools where it eliminates the need of having a receiver end. However, these tools are not fully tested in various network environment and benchmark with existing widely used ABW tools. In this paper, we perform an extensive performance analysis study on RT based tools with other widely used ABW tools by looking at the accuracy and consistency. In term of accuracy and consistency, RT based tools excel in these conditions.