5 research outputs found
Experimental Evaluation of Large Scale WiFi Multicast Rate Control
WiFi multicast to very large groups has gained attention as a solution for
multimedia delivery in crowded areas. Yet, most recently proposed schemes do
not provide performance guarantees and none have been tested at scale. To
address the issue of providing high multicast throughput with performance
guarantees, we present the design and experimental evaluation of the Multicast
Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to
channel conditions and stability, which is essential for multimedia
applications. MuDRA relies on feedback from some nodes collected via a
light-weight protocol and dynamically adjusts the rate adaptation response
time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150
nodes shows that MuDRA outperforms other schemes and supports high throughput
multicast flows to hundreds of receivers while meeting quality requirements.
MuDRA can support multiple high quality video streams, where 90% of the nodes
report excellent or very good video quality
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Estimation of LRD present in H.264 video traces using wavelet analysis and proving the paramount of H.264 using OPF technique in wi-fi environment.
While there has always been a tremendous demand for streaming video over
Wireless networks, the nature of the application still presents some challenging
issues. These applications that transmit coded video sequence data over best-effort
networks like the Internet, the application must cope with the changing network
behaviour; especially, the source encoder rate should be controlled based on
feedback from a channel estimator that explores the network intermittently. The
arrival of powerful video compression techniques such as H.264, which advance in
networking and telecommunications, opened up a whole new frontier for multimedia
communications. The aim of this research is to transmit the H.264 coded video
frames in the wireless network with maximum reliability and in a very efficient
manner. When the H.264 encoded video sequences are to be transmitted through
wireless network, it faces major difficulties in reaching the destination. The
characteristics of H.264 video coded sequences are studied fully and their capability
of transmitting in wireless networks are examined and a new approach called
Optimal Packet Fragmentation (OPF) is framed and the H.264 coded sequences are
tested in the wireless simulated environment. This research has three major studies
involved in it. First part of the research has the study about Long Range Dependence
(LRD) and the ways by which the self-similarity can be estimated. For estimating the
LRD a few studies are carried out and Wavelet-based estimator is selected for the
research because Wavelets incarcerate both time and frequency features in the data
and regularly provides a more affluent picture than the classical Fourier analysis.
The Wavelet used to estimate the self-similarity by using the variable called Hurst
Parameter. Hurst Parameter tells the researcher about how a data can behave inside the transmitted network. This Hurst Parameter should be calculated for a more
reliable transmission in the wireless network. The second part of the research deals
with MPEG-4 and H.264 encoder. The study is carried out to prove which encoder is
superior to the other. We need to know which encoder can provide excellent Quality
of Service (QoS) and reliability. This study proves with the help of Hurst parameter
that H.264 is superior to MPEG-4. The third part of the study is the vital part in this
research; it deals with the H.264 video coded frames that are segmented into optimal
packet size in the MAC Layer for an efficient and more reliable transfer in the
wireless network. Finally the H.264 encoded video frames incorporated with the
Optimal Packet Fragmentation are tested in the NS-2 wireless simulated network.
The research proves the superiority of H.264 video encoder and OPFÂżs master class
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Learning for Network Applications and Control
The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements.
There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms.
In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds.
In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics.
In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs.
We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy.
In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. 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. We leverage the lessons learned from AMuSe for WiFi and use order statistics 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 to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling