Frame Level Scheduler Research Articles

A Protocol for Reliable Real-Time Wireless Communication of Large Data Samples

In this paper, we present a middleware protocol, that transmits larger-than-a-frame data samples within application real-time and reliability requirements over a lossy wireless channel. The protocol efficiently deploys an intelligent retransmission control that exploits the extended deadline requirements of a sample for frame-level scheduling. The transmission of such samples is placed in the context of the necessary integration of such external sensor data into the decision-making process of an autonomous vehicle. Therefore, we provide parameterization and access rules for communication links in resource-managed scenarios and lay out how to integrate the protocol into the popular DDS middleware. The performance of the parameterized links to satisfy their reliability requirements is studied and compared to error protection protocols in the current software stack. We base the evaluation on an OMNeT++ simulation, whereby an established wireless error model is used. The use case is placed in the context of collaborative sensing in a valet parking environment, where external infrastructure sensor data augments the autonomous vehicle's data processing chain via a lossy wireless link. Thereby, the use case adheres to the underlying safety requirements. Results show that if the protocol's awareness of the application requirements is used for parameterization, guarantees for reliability can be provided that outperform existing solutions.

APredictive Resource Allocation for Wireless Communications Systems.

Using event-driven computer simulations, we test a predictive resource allocation scheme for broadband wireless communications systems based on the Kalman Filter. We implemented the filter in six different scheduling algorithms and selected the best performing of them (EXP Rule and FLS) for a detailed analysis. We considered several simulation scenarios with velocities ranging from 0 to 350 km/h representing pedestrian, vehicular and high-speed train scenarios. We also considered different traffic profiles by changing the number of mobile devices between 10 and 250. Our simulations showed improvement in throughput, spectral efficiency, and a decrease in packet loss rates for two schedulers: the EXP Rule and the Frame Level Scheduler. Our predictor can be applied to any wireless system that performs centralized resource allocation based on data rate or any other quantity that needs to be estimated for resource distribution in a network.

An Extensive Study on the Performance Evaluation and Scheduling of HeNBs

Since the dawn of mobile communication systems, reducing the cell size has been one option to increase the signal-to-interference-plus-noise ratio (SINR) in both links. The impact of this reduction can be perfectly understood by considering Shannon’s law. This work studies in detail the performance of Home eNBs (HeNBs), nodes with a smaller coverage area. After a detailed theoretical study of the SINR, a simulation approach is used to extract performance results in small cell indoor scenarios. Results corresponding to the goodput, delay and packet loss ratio are analyzed. Based on an improved version of LTE-Sim, the proportional fair, frame level scheduler (FLS) and exponential rule are tested in an indoor environment. With the saturation conditions taken into consideration, the FLS performs better than the other schedulers. This work shows that with the considered applications, it is possible to achieve a reduction in the transmitter power of HeNBs without compromising the small cell network performance.

An Innovative Machine-Learning-Based Scheduling Solution for Improving Live UHD Video Streaming Quality in Highly Dynamic Network Environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360°) video streaming. The proposed solution is deployed in a highly dynamic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate.

COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule. The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule. The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

PERFORMANCE ANALYSIS OF EXP-BET ALGORITHM FOR TRIPLE PLAY SERVICES IN LTE SYSTEM

Quality of Service (QoS) is defined as user’s satisfaction towards service performance that has been offered to them. Due to different traffic characteristics and QoS requirements of real-time and non-real-time services, thus provisioning the QoS requirements has become a challenge. In this study, we have compared our proposed algorithm namely the Exponential Blind Equal Throughput (EXP-BET) towards the Exponential Proportional Fairness (EXP-PF) and Frame Level Scheduler (FLS). The comparisons have been made in terms of fairness index, throughput,packet loss rate (PLR) and delay. From the simulation results, it is observed that EXP-BET delivers higher fairness and throughput and lower PLR and delay for real-time application. Instead, EXP-BET shows 17.72% improvement than FLS and 7.52% from EXP-PF in term of fairness index for the non-real-time application.

WE-MQS: A new LTE Downlink Scheduling Scheme for Voice Services based on User Perception

The Long Term Evolution (LTE) is a high data rates and fully All-IP network. Scheduling and resource allocation are very important tasks in LTE network. So that, the optimization of the real-time services transmission is very essential. However, there are many challenges when deploying the live multimedia services such as VoIP, Video, etc. This paper proposes a new scheduling scheme which based on the Wideband (WB) E-model, Channeland QoS-Aware (known as WE-MQS scheduler) for voice traffic in LTE downlink direction. The voice traffic flow is very sensitive to network impairments such as delay, packet loss, jitter, etc. The proposed scheduling scheme is based on the extension of the WB E-model and the consideration of Maximum Queue Size (MQS) as a factor for the metric. Since this scheduling scheme considers Mean Opinion Score (MOS) values, thus, it gets higher user perception. The simulation results show that the proposed scheme has the performance which not only satisfies QoS requirements of voice services but also outperforms well-known schedulers such as Frame Level Scheduler (FLS), Modified Largest Weighted Delay First (MLWDF) and Exponential/Proportional Fair (EXP/PF) schedulers in terms of delay for all the number of user (NU) and Packet Loss Rate (PLR) when the NU is more than 47. For the cell throughput, Fairness Index (FI), and Spectral Efficiency, the proposed scheduler is always in the middle of the remaining schedulers. The performance evaluation is compared in terms of Delay, PLR, Throughput, FI, and FI for FLS, M-LWDF, EXP/PF schedulers and the proposed one.

Content-Aware Concurrent Multipath Transfer for High-Definition Video Streaming over Heterogeneous Wireless Networks

Delivering high-definition (HD) wireless video under stringent delay constraint is challenging with regard to the limited network resources and high transmission rate. Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) exploits the multihoming feature of mobile devices to establish associations with different access networks. In this paper, we study the multihomed HD video communication with SCTP over heterogeneous wireless networks. The existing CMT schemes mainly treat the traffic data in a content-agnostic fashion, and thus cannot effectively leverage the scarce wireless resources to maximize the perceived video quality. To address this critical issue, we propose a content-aware CMT (CMT-CA) solution that featured by the unequal frame-level scheduling based on estimated video parameters and feedback channel status. First, we develop an analytical framework to model the total distortion of parallel video transmission over multiple wireless access networks. Second, we introduce a joint congestion control and data distribution scheme to minimize the total distortion based on online quality evaluation and Markov decision process (MDP). The performance of CMT-CA is evaluated through extensive semi-physical emulations in Exata involving HD video encoded with H.264 codec. Experimental results show that CMT-CA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), end-to-end delay, and goodput. Or conversely, CMT-CA achieves the same video quality with $20$ percent bandwidth conservation.

Comparative Analysis of Downlink Packet Scheduling Algorithms in 3GPP LTE Networks

Long Term Evolution (LTE) mobile network aims to support high speed network services even in highmobility scenarios. To achieve this goal, LTE adopts some advanced features in Radio Resource Management (RRM) procedures. Among them, LTE packet scheduling plays a fundamental role in maximising system performance. In this paper, a comparative analysis on the performances of Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Exponential (EXP) Rule, MaximumLargest Weighted Delay First (M-LWDF), Logarithmic (LOG) Rule and Frame Level Scheduler (FLS) LTE downlink packet scheduling algorithms is reported. Performance is evaluated in single cell with interference environment while increasing user number and user speed. Results show that for multimedia flow, FLS scheme outperforms other five schemes in terms of packet delay, packet loss ratio, and average throughput, whereas for best-effort flow, EXP-PF scheme shows better average throughput performance on average as compared with other algorithms being considered herein.

Performance Analysis of FLS, EXP, LOG and M-LWDF Packet Scheduling Algorithms in Downlink 3GPP LTE System

Long-Term Evolution (LTE), an emerging and promising fourth generation mobile technology, is expected to offer ubiquitous broadband access to the mobile subscribers. In this paper, the performance of Frame Level Scheduler (FLS), Exponential (EXP) rule, Logarithmic (LOG) rule and Maximum-Largest Weighted Delay First (M-LWDF) packet scheduling algorithms has been studied in the downlink 3GPP LTE cellular network. To this aim, a single cell with interference scenario has been considered. The performance evaluation is made by varying the number of UEs ranging from 10 to 50 (Case 1) and user speed in the range of [3, 120] km/h (Case 2). Results show that while the number of UEs and user speed increases, the performance of the considered scheduling schemes degrades and in both case FLS outperforms other three schemes in terms of several performance indexes such as average throughput, packet loss ratio (PLR), packet delay and fairness index.

A low-latency scheduling approach for high-definition video streaming in a heterogeneous wireless network with multihomed clients

We consider the problem of high-definition (HD) video transmission in a heterogeneous wireless network from a video server to a multihomed client. On the one hand, a single wireless network is limited in the transmission performance (e.g., available bandwidth and link delay); On the other hand, the HD video streaming is characterized by the high transmission rate and large-size video frames. Thus, the end-to-end video frame delay becomes a severely challenging problem which is critical for the real-time video applications. In this paper, we propose a novel scheduling approach named sub-frame-level (SFL), which deliberately splits the large-size video frames into sub-frames and dispatches each of them onto a different wireless network to the multihomed client. This approach is able to improve the frame-level delay for enhancing video quality. We formulate the optimization problem of video streaming allocation for minimizing the end-to-end delay based on the network calculus and derive its solution with the water filling algorithm. We evaluate the performance of the proposed SFL through the Exata emulations using real-time H.264 video streaming. Emulation results show that SFL outperforms the existing frame-level scheduling approaches in improving the frame-level delay as well as in enhancing video quality in terms of peak signal-to-noise ratio.