Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots

Schedulers for next generation wireless networks : realizing QoE trade-offs for heterogeneous traffic mixes

In this thesis we will focus on the design of schedulers for next generation wireless networks which support application mixes, characterized by different, possibly complex, application/user Quality of Experience (QoE) metrics. The central problem underlying resource allocation for such systems is realizing QoE trade-offs among various applications/users given the dynamic loads and capacity variability they would typically see. In the first part of the thesis our focus is on applications where QoE depends on flow-level delay-based metrics. We consider system-wide metrics which directly capture both users' QoE metrics and appropriate QoE trade-offs among various applications for a wide range of system loads. This approach is different from the traditional wireless scheduler designs which have been driven by rate-based criteria, e.g., utility maximizing/proportionally fair, and/or queue-based packet schedulers which do not directly reflect the link between flow-level delays and users' QoE. In the second part of this thesis we address the key design challenges in networks supporting Ultra Reliable Low Latency Communications (URLLC) traffic which requires extremely high reliability (99.999%) and very low delays (1 msec). We will explore three different types flow delay-based metrics in this proposal, based on 1) overall mean delay; 2) functions of mean delays; and, 3) mean of functions of delays. We begin by considering minimization of mean flow delay for an M/GI/1 queuing model for a wireless Base Station (BS) where the flow size distributions are of the New Better than Used in Expectation + Decreasing Hazard Rate (NBUE +DHZ) type. Such a flow size distribution have been observed in real systems and we too validate this model based on collected data. Using a combination of analysis and simulation we show that our scheduler achieves good performance for users that might correspond to interactive applications like web browsing and/or stored video streaming and is robust to variations in system loads. Next we consider a generalization of this approach where we minimize a metric based on cost functions of the mean flow delays in a multi-class system where users/flows are classified based on their respective QoE requirements and each class's QoE requirement is modeled by its respective cost function. This approach helps us model QoE more accurately and gives us more flexibility in considering QoE trade-offs among heterogeneous user classes. We optimize two different metrics based on how we average the cost functions of delays, namely, functions of mean delays; and mean of functions of delays. The former can be used when users' experiences are sensitive to mean delays and while the latter can be used when user's experience is also sensitive to higher moments of delays, e.g., variance or soft thresholds on delay. Extensive simulations confirm the effectiveness of our proposed approaches at realizing various QoE trade-offs and performance. In 5G wireless networks URLLC traffic is expected to support many applications like industrial automation, mission critical traffic, virtual traffic etc, where the wireless network has to reliability transport small packets with very high reliability and low delays. We address the following aspects related to the system design for URLLC traffic, 1) quantifying the impact of various system parameters like system bandwidth, link SINR, delay and latency constraints on URLLC 'capacity'; 2) provisioning wireless system appropriately to meet URLLC Quality of Service (QoS) requirements; and, 3) designing efficient Hybrid Automatic Repeat Request (HARQ) schemes for transmitting small packets. Further, due the heterogeneity in delay requirements between URLLC and other types of traffic, sharing radio resources between them creates its own unique challenges. We develop efficient multiplexing schemes between URLLC traffic and other mobile broadband traffic based on preemptive puncturing/superposition of the mobile broadband transmissions by URLLC transmissions.Electrical and Computer Engineerin

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks