Cross-Layer measurement on an IEEE 802.11g wireless network supporting MPEG-2 video streaming applications in the presence of interference

The performance of wireless local area networks supporting video streaming applications, based on MPEG-2 video codec, in the presence of interference is here dealt with. IEEE 802.11g standard wireless networks, that do not support QoS in according with IEEE 802.11e standard, are, in particular, accounted for and Bluetooth signals, additive white Gaussian noise, and competitive data traffic are considered as sources of interference. The goal is twofold: from one side, experimentally assessing and correlating the values that some performance metrics assume at the same time at different layers of an IEEE 802.11g WLAN delivering video streaming in the presence of in-channel interference; from the other side, deducing helpful and practical hints for designers and technicians, in order to efficiently assess and enhance the performance of an IEEE 802.11g WLAN supporting video streaming in some suitable setup conditions and in the presence of interference. To this purpose, an experimental analysis is planned following a cross-layer measurement approach, and a proper testbed within a semianechoic chamber is used. Valuable results are obtained in terms of signal-to-interference ratio, packet loss ratio, jitter, video quality, and interference data rate; helpful hints for designers and technicians are finally gained

Infrastructure dependent wireless multicast - the effect of spatial diversity and error correction

The use of multiple Access Points (APs) with one AP placed at the middle of a coverage area and the remaining placed at the edge may reduce the Packet Error Rate (PER) experienced by a group of multicast receivers. This paper shows that Spatial Diversity can augment the channel quality experienced especially by those nodes which are located farther from the Master AP, i.e. the AP at the middle, however this study also demonstrates the need for error correction scheme. The aim of this analysis is to propose a means of enhancing the infrastructure end of an IEEE 802.11n Wireless Local Area Network (WLAN), such that multicast data can be delivered reliably in order to guarantee that the received video has an adequate Peak Signal to Noise Ratio (PSNR), but with the constraint that the Medium Access Control (MAC) and the Physical (PHY) layer of the receivers are not modified, hence a legacy IEEE 802.11n node may join the multicast group and experience good Quality of Service.peer-reviewe

QoS performance analysis of bit rate video streaming in next generation networks using TCP, UDP and a TCP+UDP hybrid

The growth in users streaming videos on the Internet has led to increased demand for improved video quality and reception. In next generation networks (NGNs), such as 3G and 4G LTE, quality of service (QoS) implementation is one of the ways in which good video quality and good video reception can be achieved. QoS mainly involves following an industry-wide set of standard metrics and mechanisms to achieve high-quality network performance in respect of video streaming. Adopting routing and communication protocols is one way QoS is implemented in NGNs. This article describes QoS of bit rate video streaming, and QoS performance analysis of video streaming, in relation to the main network transport protocols, namely transmission control protocol (TCP) and user datagram protocol (UDP). A simulation test bed was set up using OPNET modeller 14.5. In this setup, a network topology was created and duplicated three times, in order to configure two simulation scenarios (each using the distinct protocols), and a third simulation scenario using both protocols in hybrid form. The findings in the simulations indicated that, when a network is configured with both TCP and UDP protocols in video streaming, there is a positive change in the degree of performance in terms of the QoS of videostreaming applications, unlike when the protocols are used independently.CA2016www.wits.ac.za/linkcentre/aji

Measurement-Driven Algorithm and System Design for Wireless and Datacenter Networks

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds)

DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs