M. Sajid Mushtaq

QoE/QoS-aware LTE downlink scheduler for VoIP with power saving

The emerging and fastest growing multimedia services such as Skype, GTalk and interactive video gaming have created new challenges for wireless communication technologies, especially in terms of resource allocation and power optimization of User Equipments (UEs). The efficient resources and the power optimization are very important in the next communication systems because new multimedia services are more resource and power hungry. Having more traffic flow in the downlink as compared to the uplink, the resource allocation schemes in the downlink are more important than those in the uplink. In this paper, we have proposed a new downlink scheduling scheme, known as Quality-aware DRX (Q-DRX) Scheme, for Long Term Evaluation (LTE) wireless networks that not only improves the Quality of Service (QoS) to satisfy the user?s experience of multimedia services but also saves the UE?s power by using the Discontinuous Reception (DRX). This paper investigates how the different durations of DRX Light and Deep Sleep cycles affect the QoS and influence the Quality of Experience (QoE) of end users, in the context of Voice over IP (VoIP) traffic delivery over the LTE wireless network. The performance of the Q-DRX scheme is evaluated with traditional scheduling schemes, in the perception of the following important parameters, System Throughput, Throughput Fairness Index, Packet Loss Rate and Packet Delay. Our proposed Q-DRX Scheme successfully minimized the packet?s delay and packet?s loss by considering the key parameters of QoS with fair resources distribution among the UEs to achieve a high satisfaction level.

QoS-Aware LTE Downlink Scheduler for VoIP with Power Saving

The addition of emerging multimedia services such as Skype, GTalk, multi-user interactive gaming in cellular communication systems has created new challenges for resource allocation and power optimization. The requirement of efficient resource allocation is more important in downlink than uplink due to high traffic flows. These multimedia applications require more power, therefore power optimization has gained a key role in future communication systems. In this paper, a downlink scheduling technique in Long Term Evolution (LTE) is proposed that improves the Quality of Service (QoS) for multimedia services in relation with use of the power saving scheme at the User Equipment (UE) i.e. Discontinuous Reception (DRX). The DRX effectively improves power consumption at the cost of QoS degradation due to higher packet delays and packet losses. The proposed scheme considers key QoS parameters during scheduling with fair resource allocation while minimizing packet delay and packet loss even in a power saving environment.

QoE-based LTE downlink scheduler for VoIP

Emerging multimedia services are spreading significantly thanks to the availability of high speed wireless networks. Network operators are facing the challenges to accommodate these services (e.g. Skype, interactive gaming, and other multimedia applications) with high user satisfaction. This paper presents a Quality of Experience (QoE) driven approach to allocate the radio resources in Long Term Evolution (LTE) wireless networks, in order to get higher user satisfaction. The main objective is to enhance the user experience while using the multimedia services such as VoIP, by jointly considering the user QoE and available wireless radio resources in the LTE-Advanced (LTE-A) network. Specifically, we propose a new downlink scheduling algorithm known as “QoE Scheme” for multimedia VoIP service in LTE-A networks. The main advantage of the proposed QoE Scheme is its measurement of user satisfaction and its feedback mechanism to the scheduler at evolving NodeB (eNodeB) in order to make the scheduling decision.

QoE Power-Efficient Multimedia Delivery Method for LTE-A

The fastest growing of multimedia services over future wireless communication systems demands more network resources, efficient delivery of multimedia service with high user satisfaction, and power optimization of user equipments (UEs). The resources and power optimization are significant in future mobile computing systems because emerging multimedia services consume more resources and power. The 4G standard of Long Term Evolution-Advanced (LTE-A) wireless system has adopted the discontinuous reception (DRX) method to extend and optimize the UE battery life, while there is no standard scheduling method to distribute the radio resources among the UE. This paper presents a downlink scheduler, i.e., Quality of Experience Power Efficient Method (QEPEM) for LTE-A, which efficiently allocates the radio resources and optimizes the use of UE power using the DRX mechanism. We investigate how the different duration of DRX Light and Deep Sleep cycle influences the Quality of Service (QoS) and QoE of end users, using voice over IP (VoIP) over the LTE-A. The QEPEM is evaluated with the traditional methods, in terms of system throughput, fairness index, packet loss rate, and packet delay. The QEPEM measures the users QoE and feeds back to the eNodeB for scheduling decisions along with other important parameters. Our proposed method reduces the packet delay and packet loss and increases the fairness and UE power saving with high user satisfaction.

QoE in 5G cloud networks using multimedia services

The 4G standard Long Term Evolution-Advanced (LTE-A) has been deployed in many countries. Now, technology is evolving towards the 5G standard since it is expecting to start its service in 2020. The 5G cellular networks will mainly contain in cloud computing and primarily Quality of Service (QoS) parameters (e.g. delay, loss rate, etc.) influence the cloud network performance. The impact of user perceived Quality of Experience (QoE) using multimedia services, and application significantly relies on the QoS parameters. The key challenge of 5G technology is to reduce the delay less than one millisecond. In this paper, we have described a method that minimizes the overall network delay for multimedia services; which are constant bit rate (VoIP) and variable bit rate (video) traffic model. We also proposed a method that measures the users QoE for video streaming traffic using the network QoS parameters, i.e. delay and packet loss rate. The performance of proposed QoE method is compared with QoV method, and our proposed QoE method performs best by carefully handle the impact of QoS parameters. The results show that our described method successfully reduces the overall network delays, which result to maximize the users QoE.

Regulating QoE for adaptive video streaming using BBF method

HTTP video streaming becomes a main contributor in the ever increasing Internet traffic. It is not an easy task for the network service provider to guarantee the best users Quality of Experience (QoE) in diverse networks with different access technologies. This requires an adaptive method that dynamically adapts the video quality service over HTTP according to time varying network conditions. In this paper, a client-based rate adaptive method is proposed that dynamically selects the appropriate video quality according to network conditions and users device properties. The proposed method considers three important Quality of Service (QoS) factors that regulate the users QoE for video streaming over HTTP, which are: Bandwidth, Buffer, and dropped Frame rate (BBF). The network bandwidth significantly affects the video service, as it directly reduces the client buffering that may result in pausing or stalling during video streaming. The proposed BBF method efficiently deals with sudden drop of network bandwidth by using the new bandwidth metric, and reduces its impact on the buffer level of the end user. The buffer length plays a vital role to handle the dynamic change in bandwidth. The dropped frame rate (fps) is another influential factor that minimizes the users QoE. The proposed BBF algorithm is evaluated with different buffer length, and it is compared to Adobes OSMF adaptive method.

Crowd-sourcing framework to assess QoE

The quality assessment of multimedia services as perceived by the end-user is a challenging task. In a controlled environment, it is easy to measure the users Quality of Experience (QoE) against the influence of controlled network parameters. However, in an uncontrolled environment, it is difficult to measure the Quality of Service (QoS) perceived by end-users, due to the unpredictable behaviour of networks. Crowd-sourcing is an emerging technique that can be employed to measure the QoE at the end user, but in an uncontrolled environment. In this paper, we present a tool implementing a crowd-sourcing framework, that measures the QoE of online video streaming, as perceived by end-users. The tool also measures important QoS network parameters in real-time (packet loss, delay, jitter and throughput), retrieves system information (memory, processing power etc.), and other properties of the end-users system. The proposed approach provides the opportunity to explore the users quality perception in a wider domain.

Power saving model for mobile device and virtual base station in the 5G era

It is a critical requirement of the future 5G communication networks to provide high speed and significantly reduce the network energy consumption. Energy efficient networks along with an energy saving strategy in mobile devices play a vital role in the mobile revolution. The new strategies should not only focus on wireless base stations, which consumes most of the power, but also considers the other power consumption elements for future mobile communication networks, including User Equipment (UE). In this paper, we have proposed a method that calculates the power consumption of a 5G network by considering its main elements based on current vision of 5G network infrastructure. Our proposed model uses the component based methodology that simplifies the process by taking into account the different high power consuming elements. The proposed method is evaluated by considering the three UEs DRX models and Virtual Base Station (VBS) with respect to different DRX timer in terms of Power Saving (PS) and delay as performance parameters.

HTTP rate adaptive algorithm with high bandwidth utilization

Video streaming over Hypertext Transfer Protocol (HTTP) is highly dominant due to the availability of Internet support on many devices. The multimedia applications that generate IP traffic should be conducive with efficient utilization of network resources. Adaptive video streaming over HTTP becomes attractive for content service providers, as it not only uses the existing infrastructure of Web downloading (thus saving an extra cost), but it also provides the ability to change the video quality (bitrate) according to dynamic network conditions for increasing the users perceived Quality of Experience (QoE). Video streaming over HTTP is easier and cheaper to move data closer to network users, and the video file is just like a normal Web object. In this paper, we have proposed a novel rate adaptive streaming algorithm that enhances the users perceived quality with high bandwidth utilization for on-demand video. The proposed algorithm considers the following metrics in order to adapt the video quality, which are; player buffer, dropped of excess video frames per second (fps), and availability of network bandwidth. The algorithm is evaluated in dynamic real time Internet environment by using the wired and wireless network at the client side.

QoE-Based Framework to Optimize User Perceived Video Quality

Video streaming has become a main contributor in an ever increasing Internet traffic, and meets the users expectation is a challenging task for both the Network service Provider (NsP) and Content service Provider (CsP). In this context, a new metric called: Quality of Experience (QoE) is evolved to measure the user satisfaction using video service, and it becomes a key driver for achieving the business goal of NsP and CsP. In this perspective, we have proposed a novel framework that considers the user QoE to adapt the video quality, named Optimized Quality of DASH (OQD). The objective of the proposed OQD framework is to optimize users experience, and maximize the bandwidth usage. A Machine Learning (ML) approach based on GRadient Boosting (GRB) method is implemented to predict the user QoE that considers three important network and application QoE Influence Factors (QoE IFs). We use the Reinforcement Learning (RL) approach to select the optimal video quality segment, which improves the user QoE. The performance of the proposed method is evaluated and compared against Greedy adaptive bit-rate method in terms of re-buffering, bandwidth utilization, average MOS, and standard deviation MOS. The results clearly show that proposed method performs well, as it considers the user’s perceived video quality as a regulator to optimize the overall video delivery network.