Bandwidth-constrained Network Research Articles

Consensus-Based Distributed State Estimation Over Sensor Networks With Encoding-Decoding Scheme: Accommodating Bandwidth Constraints

In this article, the consensus-based distributed estimation problem is investigated for linear time-invariant systems over sensor networks, where the sensors are required to estimate the system states in a cooperative manner through communication. A novel encoding-decoding scheme (EDS), which consists of two pairs of an innovation encoder/decoder and an estimation encoder/decoder, is proposed on each sensor to compress the data in order to accommodate the bandwidth-constrained network. An EDS-based consensus estimator is designed whose estimation performance is thoroughly discussed. Specially, a necessary and sufficient condition is established to ensure the convergence of the error dynamics of the state estimates, and then the boundedness issue of the size of the transmitted data is examined. Three optimization algorithms are provided for, respectively, the fastest convergence of the error dynamics, the minimization of the estimator gains, as well as the tradeoff between the convergence rate and the estimation deviation. The effectiveness of the developed distributed estimators is finally illustrated by a series of numerical examples.

RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users’ viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.

Event-triggered nonlinear filtering for networked systems with correlated noises

This paper focuses on the filtering problem for nonlinear networked systems with event-triggered data transmission and correlated noises. An event-triggered data transmission mechanism is introduced to reduce excessive measurements transmitted over a bandwidth-constrained network. Considering that process noise and measurement noise are one-step cross-correlated, an UKF-based filtering algorithm which depends on correlation parameter and trigger threshold is presented. Then sufficient conditions are established to ensure stability of the designed filter, where a critical value of the correlation parameter exists. Finally, the effectiveness of the proposed filtering algorithm is demonstrated by comparative simulations.

EQ

The rising popularity of data calls and the slowed global economy have posed a challenge to voice data networking—how to satisfy the growing user demand for VoIP calls under limited network resources. In a bandwidth-constrained network in particular, raising the bitrate for one call implies a lowered bitrate for another. Therefore, knowing whether it is worthwhile to raise one call's bitrate while other users might complain is crucial to the design of a user-centric rate control mechanism. To this end, previous work (Chen et al. 2012) has reported a log-like relationship between bitrate and user experience (i.e., QoE) in Skype calls. To show that the relationship extends to more general VoIP calls, we conduct a 60-participant user study via the Amazon Mechanical Turk crowdsourcing platform and reaffirm the log-like relationship between the call bitrate and user experience in widely used AMR-WB. The relationship gives rise to a simple and practical rate control scheme that exponentially quantizes the steps of rate change, therefore the name—exponential quantization (EQ). To support that EQ is effective in addressing the challenge, we show through a formal analysis that the resulting bandwidth allocation is optimal in both the overall QoE and the number of calls served. To relate EQ to existing rate control mechanisms, we show in a simulation study that the bitrates of calls administered by EQ converge over time and outperform those controlled by a (naïve) greedy mechanism and the mechanism implemented in Skype.

A Multi-Path-Based Adaptive Scheme for Multi-View Streaming Over HTTP

A majority of the existing research on HTTP focuses on improving the quality of experience for single-source videos. Recently, there has been a focus on multi-view streaming applications, where multiple videos are streamed simultaneously. The challenge is to improve the viewing experience of concurrently streamed videos over the bandwidth-constrained network. In this paper, we propose an adaptive scheme for multi-view streaming over HTTP that requests segments of multiple videos concurrently. First, we propose a unified throughput estimation method to calculate the throughput, solving unfair and inefficient throughput utilization problems of existing estimation methods. Second, we design a unified segment scheduling method that guarantees synchronicity among segments of concurrently downloaded video streams. Third, we present a multi-path streaming method that leverages path diversity to improve the user experience of the video streams. Finally, we present a rate adaptation algorithm that dynamically downloads high-quality segments of multiple video streams, while minimizing video quality changes and the risk of playback interruption in single- and multi-path environments.

A priority-based adaptive scheme for multi-view live streaming over HTTP

Adaptive streaming over Hypertext Transport Protocol (HTTP) has been widely used for the transmission of video content. Most of the existing studies about HTTP adaptive streaming (HAS) concentrate on improving resource efficiency, fairness among users, and quality of service for single-source videos. However, there is a growing number of live streaming applications, such as video-casts of courses, where service providers want to transmit video streams of different scenes simultaneously over the bandwidth-constrained network. Since concurrent streams further deteriorate the insufficient bandwidth situation, a systematic solution is highly needed to utilize the resources efficiently and guarantee the quality of multi-view video services. In this paper, we propose a priority-based adaptive scheme for multi-view live streaming based on HTTP streaming. Firstly, we adopt an integrated bandwidth prediction approach to calculate the available bandwidth, solving the low bandwidth utilization problem existing in the benchmark method. Secondly, we design a unified segment request strategy to restrict the interval between each request, and guarantee the synchronization of different live video streams. Finally, an adaptive scheduling algorithm is proposed with respect to priorities, to dynamically adjust the quality level of multiple video streams and improve the quality of multi-view video service. Simulations show that the proposed scheme makes considerable improvements compared to the benchmarks.

Fractal self-similarity measurements based clustering technique for SOAP Web messages

The significant increase in the usage of Web services has resulted in bottlenecks and congestion on bandwidth-constrained network links. Aggregating SOAP messages can be an effective solution that could potentially reduce the large amount of generated traffic. Although pairwise SOAP aggregation, that is grouping only two similar messages, has demonstrated significant performance improvement, additional improvements can be done by including similarity mechanisms. Such mechanisms cluster several SOAP messages that have high degree of similarity. This paper proposes a fractal self-similarity model that provides a novel way of computing the similarity of SOAP messages. Fractal is proposed as an unsupervised clustering technique that dynamically groups SOAP messages. Various experimentations have shown good performance results for the proposed fractal self-similarity model in comparison with some well-known clustering models by only consuming 31% of the clustering time required by the K-Means and 23% when using principle component analysis (PCA) combined with K-Means. Furthermore, the proposed technique has shown “better” quality clustering, as the aggregated SOAP messages have much smaller size than their counterparts.

DiffServ-based bandwidth-constrained anycast routing in a mobile IPv6 network

This paper proposes a differentiated service (DiffServ)-based bandwidth-constrained network layer anycast routing for a mobile IPv6 network. The proposed algorithm comprises three steps: (1) When moving into a new foreign network, the mobile node (MN) chooses a feasible path to easily locate the nearest server that can serve it. To perform path selection, a multi-level list structure (MLLS) is defined and used to store the available bandwidth and hop count of a path. (2) An ingress extended edge router (E-ER) uses the multi-level list algorithm (MLLA) to examine the MLLS for selecting the feasible path with the smallest number of hops that satisfies the MN bandwidth request. (3) The E-ER uses a condition of path change (CPC) variable to determine whether it should optimize the route between itself and the server. Simulation results indicate that the proposed algorithm offers better performance than the existing related approaches in terms of the average running time, request blocking rate, and reducing the control overhead of the simulated network. Copyright © 2010 John Wiley & Sons, Ltd.

Bargaining Strategies for Networked Multimedia Resource Management

Multiuser multimedia applications such as enterprise streaming, surveillance, and gaming are recently emerging, and they are often deployed over bandwidth-constrained network infrastructures. To ensure the quality of service (QoS) required by the delay-sensitive and bandwidth intensive multimedia data for these applications, efficient resource (bandwidth) management becomes paramount. We propose to deploy the well-known game theoretic concept of bargaining to allocate the bandwidth fairly and optimally among multiple collaborative users. Specifically, we consider two bargaining solutions for our resource management problem: the Nash bargaining solution (NBS) and the Kalai-Smorodinsky bargaining solution (KSBS). We provide interpretations for the two investigated bargaining solutions for multiuser resource allocation: the NBS can be used to maximize the system utility, while the KSBS ensures that all users incur the same utility penalty relative to the maximum achievable utility. The bargaining strategies and solutions are implemented in the network using a resource manager, which explicitly considers the application-specific distortion for the bandwidth allocation. We show that the bargaining solutions exhibit important properties (axioms) that can be used for effective multimedia resource allocation. Moreover, we propose several criteria for determining bargaining powers for these solutions, which enable us to provide additional flexibility in choosing solution by taking into consideration the visual quality impact, the deployed spatiotemporal resolutions, etc. We also determine the complexity of these solutions for our application and quantify the performance of the proposed bargaining-based resource strategies for different scenarios.