A cross-layer quality-oriented energy-efficient scheme for multimedia delivery in wireless local area networks

Wireless communication technologies, although emerged only a few decades ago, have grown fast in both popularity and technical maturity. As a result, mobile devices such as Personal Digital Assistants (PDA) or smart phones equipped with embedded wireless cards have seen remarkable growth in popularity and are quickly becoming one of the most widely used communication tools. This is mainly determined by the flexibility, convenience and relatively low costs associated with these devices and wireless communications. Multimedia applications have become by far one of the most popular applications among mobile users. However this type of application has very high bandwidth requirements, seriously restricting the usage of portable devices. Moreover, the wireless technology involves increased energy consumption and consequently puts huge pressure on the limited battery capacity which presents many design challenges in the context of battery powered devices. As a consequence, power management has raised awareness in both research and industrial communities and huge efforts have been invested into energy conservation techniques and strategies deployed within different components of the mobile devices. Our research presented in this thesis focuses on energy efficient data transmission in wireless local networks, and mainly contributes in the following aspects: 1. Static STELA, which is a Medium Access Control (MAC) layer solution that adapts the sleep/wakeup state schedule of the radio transceiver according to the bursty nature of data traffic and real time observation of data packets in terms of arrival time. The algorithm involves three phases– slow start phase, exponential increase phase, and linear increase phase. The initiation and termination of each phase is self-adapted to real time traffic and user configuration. It is designed to provide either maximum energy efficiency or best Quality of Service (QoS) according to user preference. 2. Dynamic STELA, which is a MAC layer solution deployed on the mobile devices and provides balanced performance between energy efficiency and QoS. Dynamic STELA consists of the three phase algorithm used in static STELA, and additionally employs a traffic modeling algorithm to analyze historical traffic data and estimate the arrival time of the next burst. Dynamic STELA achieves energy saving through intelligent and adaptive increase of Wireless Network Interface Card (WNIC) sleeping interval in the second and the third phase and at the same time guarantees delivery performance through optimal WNIC waking timing before the estimated arrival of new data burst. 3. Q-PASTE, which is a quality-oriented cross-layer solution with two components employed at different network layers, designed for multimedia content delivery. First component, the Packet/ApplicaTion manager (PAT) is deployed at the application layer of both service gateway and client host. The gateway level PAT utilizes fast start, as a widely supported technique for multimedia content delivery, to achieve high QoS and shapes traffic into bursts to reduce the wireless transceiver’s duty cycle. Additionally, gateway-side PAT informs client host the starting and ending time of fast start to assist parameter tuning. The client-side PAT monitors each active session and informs the MAC layer about their traffic-related behavior. The second component, dynamic STELA, deployed at MAC layer, adaptively adjusts the sleep/wake-up behavior of mobile device wireless interfaces in order to reduce energy consumption while also maintaining high Quality of Service (QoS) levels. 4. A comprehensive survey on energy efficient standards and some of the most important state-of-the-art energy saving technologies is also provided as part of the work

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

Delay aspects in Internet telephony

In this work, we address the transport of high quality voice over the Internet with a particular concern for delays. Transport of interactive audio over IP networks often suffers from packet loss and variations in the network delay (jitter). Forward Error Correction (FEC) mitigates the impact of packet loss at the expense of an increase of the end-to-end delay and the bit rate requirement of an audio source. Furthermore, adaptive playout buffer algorithms at the receiver compensate for jitter, but again this may come at the expense of additional delay. As a consequence, existing error control and playout adjustment schemes often have end-to-end delays exceeding 150 ms, which significantly impairs the perceived quality, while it would be more important to keep delay low and accept some small loss. We develop a joint playout buffer and FEC adjustment scheme for Internet Telephony that incorporates the impact of end-to-end delay on perceived audio quality. To this end, we take a utility function approach. We represent the perceived audio quality as a function of both the end-to-end delay and the distortion of the voice signal. We develop a joint rate/error/playout delay control algorithm which optimizes this measure of quality and is TCP-Friendly. It uses a channel model for both loss and delay. We validate our approach by simulation and show that (1) our scheme allows a source to increase its utility by avoiding increasing the playout delay when it is not really necessary and (2) it provides better quality than the adjustment schemes for playout and FEC that were previously published. We use this scheme in the framework of non-elevated services which allow applications to select a service class with reduced end-to-end delay at the expense of a higher loss rate. The tradeoff between delay and loss is not straightforward since audio sources may be forced to compensate the additional losses by more FEC and hence more delay. We show that the use of non-elevated services can lead to quality improvements, but that the choice of service depends on network conditions and on the importance that users attach to delay. Based on this observation, we propose an adaptive service choosing algorithm that allows audio sources to choose in real-time the service providing the highest audio quality. In addition, when used over the standard IP best effort service, an audio source should also control its rate in order to react to network congestion and to share the bandwidth in a fair way. Current congestion control mechanisms are based on packets (i.e., they aim to reduce or increase the number of packets sent per time interval to adjust to the current level of congestion in the network). However, voice is an inelastic traffic where packets are generated at regular intervals but packet size varies with the codec that is used. Therefore, standard congestion control is not directly applicable to this type of traffic. We present three alternative modifications to equation based congestion control protocols and evaluate them through mathematical analysis and network simulation

Performance Evaluation of LTE and LTE advanced standards for next generation mobile networks

Nel corso della trattazione sono analizzati gli standard 3GPP LTE e LTE-Advanced per la prossima generazione delle reti mobili cellulari. L'algoritmo OptiMOS, che può essere impiegato dalla Stazione Base per servire in modo efficiente connessioni VoIP, è descritto nel capitolo [8]. L’algoritmo di link scheduling Relay, finalizzato a ottimizzare le reti LTE avanzate in presenza di nodi relay è descritto nel capitolo [9]. Questo lavoro è stato presentato in adempimento parziale dei requisiti per la Laurea di Dottore di Ricerca in Ingegneria dell'Informazione presso l'ufficio informazioni Dipartimento di Ingegneria dell'Università degli Studi di Pisa, Italia

Software Defined Resource Allocation for Attaining QoS and QoE Guarantees at the Wireless Edge

Wireless Internet access has brought legions of heterogeneous applications all sharing the same resources. However, current wireless edge networks that provide Quality of Service (QoS) guar-antees that only cater to worst or average case performance lack the agility to best serve these diverse sessions. Simultaneously, software reconfigurable infrastructure has become increasingly mainstream to the point that dynamic per packet and per flow decisions are possible at multiple layers of the communications stack. In this dissertation, we explore several problems in the space of cross-layer optimization of reconfigurable infrastructure with the objective of maximizing user-perceived Quality of Experience (QoE) under the resource constraints of the Wireless Edge. We first model the adaptive reconfiguration of system infrastructure as a Markov Decision Pro-cess with a goal of satisfying application requirements, and whose transition kernel is discovered using a reinforcement learning approach. Our context is that of reconfigurable (priority) queueing, and we use the popular application of video streaming as our use case. Self declaration of states by all participating applications is necessary for the success of the approach. This need motivates us to design an open market-based system which promotes the truthful declaration of value (state). We show in an experimental setup that the benefits of such an approach are similar to those of the learning approach. Implementations of these techniques are conducted on off-the-shelf hardware, which have inherent restrictions on reconfigurability across different layers of the network stack. Consequently, we exploit a custom hardware platform to achieve finer grained reconfiguration capabilities like per packet scheduling and develop a platform for implementation and testing of scheduling protocols with ultra-low latency requirements. Finally, we study a distributed approach for satisfying strict application requirements by leveraging end user devices interested in a shared objective. Such a system enables us to attain the necessary performance goals with minimal use of centralized infrastructure