Accurate Throughput Prediction (TP) represents a real challenge for reliable adaptive streaming in challenging mediums, such as cellular networks. State-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy for various multimedia systems. This article illustrates that designing black-box TP engines that depend solely on the model’s capacity and power of learning does not achieve consistent accuracy across all throughput ranges. Additionally, we propose MATURE, a novel multistage DL-based TP model designed to capture network operating context to improve prediction accuracy. MATURE’s prediction involves characterizing the operating context before estimating the network throughput. We show that MATURE delivers consistent, accurate prediction for all throughput ranges in both 4G and 5G networks. We also show that light-weight MATURE models that use quantized parameters maintain their accuracy while featuring up to 100× faster inference, thus making them suitable for mobile implementation. Our real video streaming experiments further show that MATURE improves the average user Quality of Experience by up to 20% when compared to other TP methods.

WiFi 7 IEEE (802.11be) introduced Multi-Link Operation (MLO) that enables its devices to communicate over multiple links to support evolving latency-sensitive and high-throughput applications. However, MLO requires advanced scheduling algorithms to optimize the operation. This paper models WiFi 7 scheduling as a constrained Markov Decision Process that optimizes throughput, delay, latency and fairness while capturing access constraints and traffic dynamics. We also develop an attention-enhanced Rainbow Deep Q-Network (DQN) scheduling framework that combines multi-head attention, distributional Q-learning, and prioritized experience replay. Simulation results show up to 2.3× throughput improvement, 6× delay reduction, and marked gains in packet drop rate and spectral efficiency over baseline Round Robin scheduling.

Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.

Edge computing is evolving to include heterogeneous compute nodes with distinct characteristics. Graphic processing units (GPU) and field-programmable gate arrays (FPGA) can execute demanding deep learning (DL) tasks while meeting the deadlines of time-sensitive applications. However, FPGAs require reconfiguration to execute different tasks. In this paper, we first demonstrate that FPGAs can be reconfigured in real-time. Additionally, we propose ELEVATE as a novel scheduling algorithm for reconfigurable heterogeneous edge computing platforms targeting Industry 4.0 post-production quality control. ELEVATE design focusses on optimising the reconfiguration of the FPGA unit for heterogeneous quality inspection tasks. Our simulations indicate that ELEVATE reduces task waiting time by up to two orders of magnitude and achieves energy savings of up to 25 % compared to a statically configured FPGA unit.

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4% - 90% in critical network conditions when compared to state-of-the-art.

Cable networks are embracing Distributed Access Architectures (DAA) that push traditionally centralized network functions to the network edge. While this shift offers higher data rates, it complicates the management and configuration of the network by having functions distributed in remote nodes. Separately, SDN has evolved for enabling programmable networks of distributed switches, managed in a logically centralized manner. This paper presents an evolutionary path for SDN-based cable DAA that can overcome the aforementioned challenges and support new network services. We present an SDN-DAA architecture, implemented in a real DAA remote device, and evaluated using Mininet to demonstrate the operational benefits.

Different industries are observing the positive impact of 360 video on the user experience. However, the performance of VR systems continues to fall short of customer expectations. Therefore, more research into various design elements for VR streaming systems is required. This study introduces a SW tool that offers straight-forward encoding platforms to simplify the encoding of DASH VR videos. In addition, we developed a dataset composed of 9 VR videos encoded with seven tiling configurations, four segment durations, and up to four different bitrates. A corresponding tile size dataset is also provided, which can be utilised to power network simulations or trace-driven emulations. We analysed the traffic load of various films and encoding setups using the dataset that was presented. Our research indicates that, while smaller tile sizes reduce traffic load, video decoding may require more computational power.

Abstract The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns. In summary, in this paper, we present an automated data efficient approach based on weak supervision and deep learning (BERT + WS) for identification of news articles about economical uncertainty and adapt the calculation of EPU to the proposed strategy. Experimental results reveal that our approach (BERT + WS) improves over the baseline method centered in keyword search, which is currently used to construct the EPU index. The improvement is over 20 points in precision, reducing the false positive rate typical to the use of keywords.

Evolving Internet applications, such as immersive multimedia and Industry 4, exhibit stringent delay, loss, and rate requirements. Realizing these requirements would be difficult without advanced dynamic traffic management solutions that leverage state-of-the-art technologies, such as Software-Defined Networking (SDN). Mininet represents a common choice for evaluating SDN solutions in a single machine. However, Mininet lacks the ability to emulate links that have multiple queues to enable differentiated service for different traffic streams. Additionally, performing a scalable emulation in Mininet would not be possible without light-weight application emulators. In this paper, we introduce two tools, namely: QLink and SPEED. QLink extends Mininet API to enable emulating links with multiple queues to differentiate between different traffic streams. SPEED represents a light-weight web traffic emulation tool that enables scalable HTTP traffic simulation in Mininet. Our performance evaluation shows that SPEED enables scalable emulation of HTTP traffic in Mininet. Additionally, we demo the benefits of using QLink to isolate three different applications (voice, web, and video) in a network bottleneck for numerous users.

Evolved Multimedia Broadcast Multicast Service (eMBMS) is used in cellular networks to improve the utilization of scarce wireless resources in high user density service areas. However, eMBMS configuration involves interwoven decisions including which base stations (eNB) to synchronize to form Single Frequency Networks (SFN), which video qualities to be serviced, and how to distribute resources among different videos. These decisions should accommodate disparate channel conditions for eMBMS users, and the impact of eNB's unicast-load in the service area. In this paper, we formulate eMBMS configuration as an optimization problem that maximizes the video QoE for users. Additionally, we present NIMBLE as an eMBMS configuration heuristic, guided by our optimization framework, to solve the problem in realtime. Furthermore, NIMBLE's design integrates elements to accommodate the dynamic nature of cellular networks resulting from changes in both user, and network state over time. We developed a simulation testbed, and performed extensive experiments to show that, in comparison to state-of-the-art schemes, NIMBLE can increase the average user throughput by 150%, and reduce the bitrate switches by 75%.

In this paper we present Joint Unicast-Multicat Panoramic Streaming (JUMPS) over the cellular network. JUMPS optimizes the resource allocation for a group of eMBMS (evolved Multicast Broadcast Multimedia Systems) users to enhance their experience while leveraging the inherent diversity in both users' network conditions and field of view (FoV). The key intuition is combining unicast and multicast, for tiled panoramic content, would enable facilitate using the right amount of resources for every tile considering the tile popularity and receiving user link quality. We compare JUMPS performance to state-of-the-art solutions and show that it significantly improves users' received FoV bitrate and reduces their battery by reducing the number of resource blocks that users have to listen to. These results are consistent across various scenarios that vary across user group link conditions, FoV diversity, and available network resources.

Evolved Multimedia Broadcast Multicast Service (eMBMS) is a 3GPP standard that improves the utilization of scarce wireless resources and the quality of the received content. eMBMS uses a Single Frequency Network (SFN) to transmit real-time videos over synchronized resources across neighboring base stations (eNBs) and allows users to share wireless spectrum across multiple cell sites. However the user with the worst channel condition and the eNB with the least available resources limit the throughput of a session. To overcome such limitations, the SFN can be divided into non-overlapping clusters of eNBs and in each cluster users can be split into groups. We formulate an optimization problem that maximizes an operator-defined utility for multiple eMBMS sessions served at multiple bitrates by choosing the optimal set of SFN clusters and user groups for each session. We propose an algorithm, RTOP, that finds the optimal or a near-optimal solution in real-time regardless of the number of eMBMS users. Our extensive simulations indicate that, in comparison to state-of-the-art schemes, RTOP improves the system utility and average user bitrate by up to 14% and 90% respectively. Additionally, we show that the utility of RTOP always stays within a 1% gap from the optimal solution.

The emerging popularity of live streaming services poses a great challenge for the rigid and static traditional Internet architecture. The rise in adaptation of Software Defined Networking (SDN) by Internet Service Providers (ISP) and Content Delivery Networks (CDN) presents an opportunity to dynamically adapt and respond in real-time to high definition (HD) mega events or dynamic short-lived broadcast events. In this paper, we present an SDN-based system design that utilizes a communication framework between ISPs and CDNs to interact and thus enable a reliable and resource efficient live streaming service. We build and deploy an optimization model that can maximize the video quality for users while minimizing the resource utilization for both ISPs and CDNs. The model considers device capabilities, network constraints and the subscription level of users with the ISP/CDN. Our system is a network-assisted, cross-layer, approach that implements multicast at the network layer and can dynamically adapt the video bitrates that are served to each client at the application layer. We build a prototype of our proposed design and evaluate real-world scenarios with up to 500 users streaming multiple videos at different bitrates. Results show that our approach can increase average user goodput by up to 70% while almost eliminating frame drops by handling network congestion.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

In this paper, we present a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, bus and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 4G networks.

In this paper we introduce a client emulator for experimenting with DASH video. dashc is a standalone, compact, easy-to-build and easy-to-use command line software tool. The design and implementation of dashc were motivated by the pressing need to conduct network experiments with large numbers of video clients. The highly scalable dashc has low CPU and memory usage. dashc collects necessary statistics about video delivery performance in a convenient format, facilitating thorough post hoc analysis. The code of dashc is modular and new video adaptation algorithm can easily be added. We compare dashc to a state-of-the art client and demonstrate its efficacy for large-scale experiments using the Mininet virtual network.

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error.

Dynamic adaptive streaming over HTTP (DASH) is widely adopted for video transport by major content providers. However, the inherent high variability in both encoded video and network rates represents a key challenge for designing efficient adaptation algorithms. Accommodating such variability in the adaptation logic design is essential for achieving a high user quality of Experience (QoE). In this paper, we present ARBITER+ as a novel adaptation algorithm for DASH. ARBITER+ integrates different components that are designed to ensure a high video QoE while accommodating inherent system variabilities. These components include a tunable adaptive target rate estimator, hybrid throughput sampling, controlled switching, and short-term actual video rate tracking. We extensively evaluate the streaming performance using real video and cellular network traces. We show that ARBITER+ components work in harmony to balance temporal and visual QoE aspects. Additionally, we show that ARBITER+ enjoys a noticeable QoE margin in comparison to state-of-the-art adaptation approaches in various operating conditions. Furthermore, we show that ARBITER+ also achieves the best application-level fairness when a group of mobile video clients shares a cellular base station.

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.

The rise of Software Defined Networking (SDN) presents an opportunity to overcome the limitations of rigid and static traditional Internet architecture and provide services like network layer multicast for live video streaming. In this paper we propose mCast, an SDN-based architecture for live streaming, to reduce the utilization of network and system resources for both Internet Service Providers (ISP) and Content Delivery Networks (CDN) by using multicast over the Internet. We propose a communication framework between ISPs and CDNs to enable mCast while retaining user and data privacy. mCast is transparent to the clients and maintains the control of CDNs on user sessions. We developed a testbed and performed large scale evaluation and comparison. Results showed that mCast can improve the video quality received by clients and, for CDNs and ISPs in comparison to IP unicast, mCast can decrease link utilization by more than 50% and network losses to 0%.

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric.

In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies.

In this demonstration we present a platform that encompasses all of the components required to realistically evaluate the performance of Dynamic Adaptive Streaming over HTTP (DASH) over a real-time NS-3 simulated network. Our platform consists of a network-attached storage server with DASH video clips and a simulated LTE network which utilises the NS-3 LTE module provided by the LENA project. We stream to clients running an open-source player with a choice of adaptation algorithms. By providing a user interface that offers user parametrisation to modify both client and LTE settings, we can view the evaluated results of real-time interactions between the network and the clients. Of special interest is that our platform streams actual video clips to real video clients in real-time over a simulated LTE network, allowing reproducible experiments and easy modification of LTE and client parameters. The demonstration showcases how changes in LTE network settings (fading model, scheduler, client distance from eNB, etc.), as well as video-related decisions at the clients (streaming algorithm, quality selection, clip selection, etc.), can impact on the delivery and achievable quality.

Dynamic adaptive video over HTTP (DASH) is fast becoming the protocol of choice for content providers for their online video streaming delivery. Concurrently, dependence on cellular Long Term Evolution (LTE) networks is growing to serve user demands for bandwidth-hungry applications, especially video. Each LTE base station's (eNodeB) scheduler assigns wireless resources to individual clients. Several alternative schedulers have been proposed, especially to meet the user's desired quality of experience (QoE) with video. In this paper, we investigate the impact of the scheduler on DASH performance, motivated by the fact that video performance and the underlying traffic models are different from other HTTP/TCP applications. We use our laboratory testbed employing real video content and streaming clients, over a simulated ns-3 LTE network. We quantify the impact of the scheduler and show that it has a significant impact on key video streaming performance metrics such as stalls and QoE, for different client adaptation algorithms. Additionally, we show the impact of user mobility within a cell, which has the side-effect of improving performance by mitigating long-term fading effects. Our detailed assessment of four LTE schedulers in ns-3 shows that the proportional fair scheduler achieves the best overall user experience, although somewhat disadvantaging static cell-edge users.

In this paper we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). Our trace-based simulation dataset provides a means of evaluation in frameworks such as NS-2 and NS-3, while our testbed evaluation dataset offers a means of analysing the delivery of content over a physical network and associated adaptation mechanisms at the client. Our datasets are available in both H.264 and H.265 with encoding rates comparative to the representations and resolutions of content distribution providers such as Netflix, Hulu and YouTube.

Network utility theory has been extensively employed for resource management purposes. However, traditional utility functions cannot support attributes that affect the resource allocation, such as mobility or more advanced traffic models. Especially in the context of a heterogeneous wireless network, transient parameters can have varying effects on each access network type. This work proposes a new utility function that can support multiple design requirements for mobile networks including advanced traffic models, user classes, handover and session priorities. We integrate the new utility function with the Super Base Station framework and devise a novel trigger-based network and resource assignment framework that efficiently copes with the complexity of a heterogeneous wireless network. Our simulation results show that the proposed sub-optimal trigger-based framework performs equally well as the complex optimal scheme.

In this paper we study the problem of secondary user channel access in cognitive radio networks. In particular, we address the problem of deciding the secondary user sensing vs. transmission at any point of time, assuming the availability of the primary user idle time statistics. Towards this objective, we make the following contributions. First, unlike prior work, we assume unconstrained general idle time distribution for the primary user under secondary user imperfect sensing and imperfect collision detection. Second, we propose a novel approach grounded in reliability theory to analyze the time based activity of the primary user. Finally, motivated by the sheer complexity of the problem, we propose three heuristic schemes for deciding the secondary user sensing/transmission actions at any point of time. We conduct computer simulations to evaluate the performance of the proposed schemes and compare them to the traditional per-packet sensing/transmission scheme and to each other. Our numerical results reveal that, for an experimentally verified idle time distribution in heterogeneous network activity, at least one of our proposed schemes can achieve 27% throughput increase and down to 3.85%, under different QoS requirements for the PU. Also we show through simulations that our heuristic schemes are very close to optimal, when the optimal scheme can be applied.

HTTP streaming currently dominates Internet traffic. It is increasingly common that video players employ adaptive bitrate (ABR) streaming strategies to maximise the user experience by selecting the highest video representation while targeting stall-free playback. Our interest lies in the common situation where a set of video flows are competing for access to a shared bottleneck link, such as in a cellular radio access network. We observe that ISPs (e.g. cellular operators) are considering innetwork techniques for resource allocation and sharing among different users. Buoyed by the ability of software defined networks (SDN) to offer flow-specific control and traffic shaping, we focus on traffic shaping techniques, and experimentally analyse the effect on ABR video flows when sharing a bottleneck link. We conduct experiments using the GPAC video player operating over a Mininet virtual network. We conclude that traffic shaping can allow a balance of fairness, efficiency and quality. Traffic shaping ABR videos reduce the number of stalls and quality switches, while also reducing the peaks for the aggregate network traffic.

Energy efficient operation of cellular systems becomes a core design goal for economic and environment-friendly network operation. Several studies have shown that the energy consumed in base stations represents 60-80% of the energy consumption in cellular networks. In this paper, we develop an optimization framework that exploits several energy efficient techniques including switching power modes of base stations, Adaptive Modulation (AM), and the use of relays. Our main objective is to reduce both, transmitted and circuit power, subject to satisfying the quality of service constraints. To accommodate the complexity of the target problem, we further propose two sub-optimal algorithms, minimum power heuristic (MPH) and minimum relays heuristic (MRH). The simulation results show that energy saving merits of our proposed schemes can be up to 80%.

Cognitive radio technology would enable a set of secondary users (SU) to opportunistically use the spectrum licensed to a primary user (PU). On the appearance of this PU on a specific frequency band, any SU occupying this band should free it for PUs. Typically, SUs may collaborate to reduce the impact of cognitive users on the primary network and to improve the performance of the SUs. In this paper, we propose and analyze the performance of virtual reservation in collaborative cognitive networks. Virtual reservation is a novel link maintenance strategy that aims to maximize the throughput of the cognitive network through full spectrum utilization. Our performance evaluation shows significant improvements not only in the SUs blocking and forced termination probabilities but also in the throughput of cognitive users.

Resource management in heterogeneous wireless networks has been approached from various angles by the research community. The complexity of the network and the heterogeneity of clients make the conclusive comparison of the various resource allocations challenging. This work introduces superBS, an approach defining a theoretical optimal resource allocation that adheres to the required resource management policies and can be used as a reference for the performance of considered algorithms, mitigating the heterogeneity of the system. Two applications that leverage superBS are developed, an implementation of a heuristic resource management algorithm, and the enhancement of a popular fairness metric with support for clients of different classes and traffic demands. Simulations demonstrate the performance of superBS and the proposed algorithms.

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality.

No abstract available.

Heterogeneous long term evolution (LTE) networks evolve as a possible solution to accommodate the exponential growth of mobile data. However, the heterogeneity introduces several design challenges such as increasing interference and mobility overhead. In this paper, we propose Extended Synchronization Signals (ESS) to eliminate the unavoidable physical cell identity (PCI) confusion problem in dense heterogeneous LTE deployments. The ESS is also designed to reduce signaling overhead and handover delay. Our analysis shows a handover delay reduction of 65% can be realized. Additionally, we show that PCI confusion probability can be practically eliminated in case of centralized planning and a reduction of five order of magnitude in confusion probability can be attained in case of distributed planning.

The convergence of heterogeneous wireless access technologies is an intrinsic part of the long term evolution of wireless networks. This convergence creates network overlays in which achieving seamless and efficient roaming between different technologies, commonly known as vertical handoff (VHO), introduces several challenging design issues. The media independent handover standard (IEEE 802.21) enables information exchange across different layers to improve VHO performance. In this work, we present a framework for integrating an adaptive lifetime-based vertical handoff (ALIVE-HO) algorithm with IEEE 802.21. The proposed framework is implemented and the optimal design value of the application parameter of ALIVE-HO is experimentally determined for video streaming applications under different operating scenarios.

The integration of different wireless access technologies combined with the huge characteristic diversity of supported services in next-generation wireless systems creates a real heterogeneous network. In this paper, we propose a generic practical framework that optimizes media streaming in heterogeneous systems by taking advantage of cost and resource characteristic diversity of the integrated access technologies and the buffering capability of streaming applications. The proposed optimization framework represents a means to compromise the tradeoff between different performance metrics including streaming monetary cost, signaling load, and session quality. Additionally, it accommodates different design challenges including mobility randomness, limited processing capacity, and handoff delay requirements. The simulation results provide important insights on the design of pricing profiles in integrated systems. Additionally, the results show that significant cost savings can be realized using the newly proposed streaming management algorithms and optimization framework.

Presents the introductory welcome message from the conference proceedings.

The integration of different wireless access technologies combined with the huge characteristic diversity of supported services in next-generation systems creates a real heterogeneous system. This heterogeneity opens new avenues for improving the system utility of both operators and users. In this paper, we propose a Pseudo-optimal greedy media streaming (PGMS) algorithm to reduce the cost of streaming sessions using an optimization framework that considers the real-time requirements of the vertical handoff decision. The proposed algorithm results in noticeable reductions in session cost, signaling load, and blocking probability in comparison to previously proposed heuristics. More importantly, the results show the adaptability of PGMS to different operating scenarios including different mobility patterns and service cost profiles.

The future-generation wireless systems will combine heterogeneous wireless access technologies to provide mobile users with seamless access to a diverse set of applications and services. The heterogeneity in this inter-technology roaming paradigm magnifies the mobility impact on system performance and user perceived service quality, necessitating novel mobility modeling and analysis approaches for performance evaluation. In this paper, we present and compare three mobility models in two-tier integrated heterogeneous wireless systems, the independence model as a naive extension of the traditional cell residence time modeling techniques for homogeneous cellular networks, the basic Coxian model which takes into consideration the correlation between the residence time within different access technologies, and the extended-Coxian model for further improved estimation accuracy. We propose a general stochastic performance analysis framework based on application session models derived from these mobility models, applying it to a 3G-WLAN integrated system as an example. Our numerical and simulation results demonstrate the general superiority of Coxian-based mobility modeling over the independence model. Furthermore, using the proposed modeling and analysis methods, we investigate the impact of different parameters on system performance metrics such as network utilization time, handoff rates, and forced termination probability, for a wide range of user applications.

The next generation (4G) wireless network is envisioned as a convergence of different wireless access technologies providing the user with the best anywhere anytime connection and improving the system resource utilization. The integration of wireless local area network (WLAN) hotspots and the third generation (3G) cellular network has recently received much attention. While the 3G-network can provide global coverage with a low data-rate service, the WLAN can provide a high data-rate service within the hotspots. Although increasing the underlay network utilization is expected to increase the user available bandwidth, it may violate the quality-of-service (QoS) requirements of active real-time applications. Hence, achieving seamless handoff between different wireless technologies, known as vertical handoff (VHO), is a major challenge for 4G-system implementation. Several factors, such as application QoS requirements and handoff delay, should be considered to realize an application transparent handoff. We present a novel framework to evaluate the impact of VHO algorithm design on system resource utilization and user perceived QoS. We used this framework to compare the performance of two different VHO algorithms. The results show a very good match between simulation and analytical results. In addition, it clarifies the tradeoff between achieving high resource utilization and satisfying user QoS expectations.

This paper presents a novel mobility modeling approach for a two-tier integrated wireless system that accommodates the system complexity represented by the residence-time correlation between different access networks. Additionally, a novel session model is presented as an adapted version of the proposed mobility model. Furthermore, we develop an analytical framework using this session model to obtain several salient performance metrics such as network utilization times and handoff rates. Simulation results demonstrate that the proposed mobility model is substantially more accurate than existing modeling techniques, and that the proposed analytical framework provide tractable performance evaluation based on the new mobility model.