MEC Resource Offloading for QoE-Aware HAS Video Streaming

We develop and study adaptive rate scheduling mechanisms over cellular wireless networks, as used for the unicast provision of video streams to client mobiles at variable quality of experience (QoE) levels. Under service type I, mobile users receive video streams at a QoE level that is not lower than a specified value. In addition, mobile users that experience sufficiently high communications channel quality levels, may be provided video streams at higher video quality levels. Under service type II, mobile clients receive their video streams at QoE levels that are based on their recorded signal to noise and interference (SINR) levels. Resource allocations among mobiles are however subjected to absolute and proportional fairness objectives. We employ a proxy video manager and resource controller that is located at (or associated with) the base station node. The manager intercepts a channel quality indicator (CQI) message reported by a mobile client, using it to determine the QoE level at which a requested video stream will be provided. It then selects the proper source and channel encoding schemes to be used for producing and transmitting a compressed version of the stream. To regulate inter-cell signal interference, we examine the joint employment of a number of different spectral-reuse and fractional frequency reuse (FFR) scheduling schemes. To illustrate the use of our models to configure system parameters, we consider a performance metric that incorporates a will-topay utility function. We develop analytical techniques for the modeling, analysis and design of such systems. We confirm the precision of these models through the conduct of simulation analyses. Also, we show that, under certain system configurations, FFR-based schemes can lead to substantial enhancement of the system’s performance behavior.

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

This paper investigates the issue of Quality of Experience (QoE) for multimedia services over heterogeneous networks. A new concept of “Isomorphism Flow” (iFlow) was introduced for analyzing multimedia traffics, which is inspired by the abstract algebra based on experimental research. By using iFlow, the multimedia traffics with similar QoE requirements for different users are aggregated. A QoE evaluation method was also proposed for the aggregated traffics. Then a new cross-domain QoE guarantee method based on the iFlow QoE is proposed in this paper to adjust the network resource from the perspective of user perception. The proposed scheme is validated through simulations. Simulation results show that the proposed scheme achieves an enhancement in QoE performance and outperforms the existing schemes.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

The massive growth of data consumption and the variety of wireless technology emergence has made the handover (HO) an attractive research topic nowadays, mainly due to the popularity of Wireless Local Area Networks (WLANs), which allow users to reach high-speed data communication while they are in movement. Moreover, mobile devices such as tablets and smartphones have also become increasingly popular due to their low cost and ease of use, and an increase in mobile device use is expected to accelerate in the coming years, along with the availability and use of applications such as real-time services and online gaming. The traditional HO methods will likely not meet the requirements of mobile devices for modern applications due to the lack of intelligence, lack of awareness Quality of Service (QoS) and Quality of Experience (QoE) requirements of mobile users. We, therefore, introduce a novel architecture that supports horizontal HO in homogenous networks. This architecture is based on the Software-Defined Wireless Networking (SDWN) concept, where the wireless network is controlled centrally and the wireless Access Points (APs) are programmable. In this architecture, HO algorithms will assist wireless users to find the network that could best support the application requirements through Quality of Service (QoS) and Quality of Experience (QoE) management policies. The first HO algorithm proposed in this thesis is called Quality of Experience Oriented Handover Algorithm. This algorithm will guarantee the best possible connectivity to the users in terms of their QoE and QoS requirements and outperforms the traditional methods in a sparse network environment. The second contribution is called Optimised Handover Algorithm for Dense WLAN Environments. This algorithm has been designed to address dense network environments via taking into consideration the Adaptive Hysteresis Value (AHV). The AHV will help the Optimised Handover Algorithm via reducing the so-called ping-pong effect. This contribution shows promising performance results by selecting the best candidate AP, decreasing the number of redundant HO and avoiding the ping-pong effect. The final contribution is called Priority Based Handover Algorithm. We extended our proposed SDWN architecture in order to include the concept of prioritising users and make a smart decision during the process of HO. This algorithm will prioritise a certain class of users to avoid the effect of the over-congestion. The results show that the approach based on priority outperforms the state of the art and provides better QoE to the high priority users despite the over-congestion situation.

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users' Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users' QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.

The rapid growth of mobile services and Internet of Things (IoT), have caused a severe demand of a management system for Mobile Edge Computing (MEC) where User Equipments (UEs) benefit from high computational power, capacity and communication as well as the offered services by MEC. However, a comprehensive management is required to orchestrate the services and resource in MEC to fairly distribute to UEs with the aim of ensuring the Quality of Experience (QoE). In this paper, we propose a new and trustable framework for MEC management/orchestration system with crucial security and authentication components by which it ensures the delivery of users’ quality of experience.

HTTP video streaming employs the TCP protocol as its transport protocol, which provides reliability on one hand, but delay or delay jitter on the other hand. IEEE 802.11 MAC protocol is assumed to support wireless networks, whose channel access efficiency impacts the performance of the upper layer, i.e., the data rate and the delay of TCP. In this paper, an analytical model for Quality of Experience (QoE) of HTTP video streaming over single-hop wireless networks is proposed. Particularly, two QoE performance metrics are evaluated, which are underflow times and fluency of playout, defined as the buffer empty times during playout, and the ratio of ideal watching time to the real watching time. The impacts of system parameters, such as the length of a video file, the playout threshold, the playout rate, and the arriving rate of a TCP streaming, on the system performance metrics are analyzed in detail. It is found that when both the TCP congestion window and the MAC contention window are set to one, the arriving rate of a TCP streaming can achieve its maximum, which results in the optimal QoE performance. Moreover, the optimal setting for the MAC contention window is also obtained for the case that the TCP congestion window is larger than one. The accuracy of the proposed analytical model is verified by extensive simulations.

Tile-based 360-degree video (360 video) streaming, employed with adaptive bitrate (ABR) algorithms, is a promising approach to offer high video quality of experience (QoE) within limited network bandwidth. Existing ABR algorithms, however, fail to achieve optimal performance in real-world fluctuated network conditions as they heavily rely on unbiased bandwidth predictions. Recently, reinforcement learning (RL) has shown promising potential in generating better ABR algorithms in 2D video streaming. However, unlike existed work in 2D video streaming, directly applying RL in the tile-based 360 video streaming is infeasible due to the resulting exponential decision space. To overcome these limitations, we propose in this paper 360SRL, an improved ABR algorithm employing Sequential RL (360SRL). Firstly, we reduce the decision space of 360SRL from exponential to linear by introducing a sequential ABR decision structure, thus making it feasible to be employed with RL. Secondly, instead of relying on accurate bandwidth predictions, 360SRL learns to make ABR decisions solely through observations of the resulting QoE performance of past decisions. Finally, we compare 360SRL to state-of-the-art ABR algorithms using trace-driven experiments. The experiment results demonstrate that 360SRL outperforms state-of-the-art algorithms with around 12% improvement in average QoE.

This paper proposes a Quality of Experience (QoE) test methodology for adaptive streaming service. We analyze the factors influencing the QoE of adaptive streaming, and evaluate the QoE of the end-to-end service. Our research gives the evidence that adaptive streaming improves end-users' subjective perception greatly compared with fixed-rate streaming in terms of QoE. Two groups of experiments are conducted, one assesses artificially spliced video samples in variable bitrates, and another evaluates Dynamic Adaptive Streaming over HTTP (DASH) [1] [2] video samples under real network traces. The former experiment focuses on the influence from the bitrate distribution without considering the fluency. And the latter pays more attention to the influence of the fluency on QoE performance. Through a large number of subjective assessments, the QoE results are obtained to illustrate various impact factors on adaptive streaming, such as fluency, bitrate distribution, startup bitrate level, bitrate switching frequency, etc. The results of this study can be used to facilitate the research on mathematical modeling of user subjective experience and the algorithm development for adaptive streaming.

Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p<0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates.

The reduction of greenhouse gas emissions in the Internet and ICT sectors has become a critical challenge. According to recent research, the key contributors to greenhouse gas emissions in Internet include high energy consumption factors such as data centers, transmission network devices, and end-user devices. Among Internet services, video streaming is one of the services having the highest traffic volume and number of users. Consequently, developing energy-efficient solutions for video streaming networks, particularly for end-user devices, is an urgent research priority. Reducing energy consumption in end-user devices in a video streaming system often requires compromises in parameters that impact the quality of user experience (QoE). Therefore, achieving an optimal trade-off between minimizing energy consumption and maintaining an acceptable QoE is a key objective. In this study, a cost function that integrates QoE and energy consumption is developed using the Lagrange multiplier method. Based on this function, an adaptive bitrate algorithm is proposed to select optimal video segments for video players, ensuring maximum QoE while minimizing energy consumption. The performance of the proposed method is evaluated using various types of video samples under varying network bandwidth conditions. Experimental results show that the proposed method reduces energy consumption of end-user devices by up to 6.7% and enhances QoE by 20% compared to previous methods.

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

Over the past few years, life expectancy has increased significantly. However, elderly individuals living independently often require assistance due to mobility issues, symptoms of dementia, or other health-related challenges. In these situations, high-quality elderly care systems for the aging population require innovative approaches to guarantee Quality of Service (QoS) and Quality of Experience (QoE). Traditional remote elderly care methods face several challenges, including high latency and poor service quality, which affect their transparency and stability. This paper proposes an Edge Computational Intelligence (ECI)-based haptic-driven ECI-TeleCaring system for the remote caring and monitoring of elderly people. It utilizes a Software-Defined Network (SDN) and Mobile Edge Computing (MEC) to reduce latency and enhance responsiveness. Dual Long Short-Term Memory (LSTM) models are deployed at the edge to enable real-time location-aware activity prediction to ensure QoS and QoE. The results from the simulation demonstrate that the proposed system is proficient in managing the transmission of data in real time without and with an activity recognition and location-aware model by communication latency under 2.5 ms (more than 60%) and from 11∼12 ms (60∼95%) for 10 to 1000 data packets, respectively. The results also show that the proposed system ensures a trade-off between the transparency and stability of the system from the QoS and QoE perspectives. Moreover, the proposed system serves as a testbed for implementing, investigating, and managing elder telecaring services for QoS/QoE provisioning. It facilitates real-time monitoring of the deployed technological parameters along with network delay and packet loss, and it oversees data exchange between the master domain (human operator) and slave domain (telerobot).

Facing the ever-increasing demand for high quality video streaming and the dramatic variations of wireless network environment, adaptive bitrate video streaming (ABS) emerges as a prominent video quality adaptation technique in improving users' quality of experience (QoE). Reinforcement learning (RL)-based ABS and network-assisted ABS can improve users' QoE through resource utilization efficiency enhancement. However, limited network capacity is still the bottleneck of QoE improvement. Considering the potential capacity gain of non-orthogonal multiple access (NOMA), we study network-assisted ABS in NOMA networks. Based on a synchronous video streaming model and the knowledge of channel state information for next segment, a joint optimization problem of resource allocation and bitrate adaptation is formulated to maximize users' QoE and minimize energy consumption. The original problem is NP-hard and intractable due to the interference coordination problem introduced by NOMA and the multi-slot sum rate constraint. We decompose the original problem into the subproblem of joint subchannel assignment and power allocation (JSAPA), and the subproblem of bitrate adaptation (BA) with Lagrange relaxation method. A joint resource allocation and bitrate adaptation (JRABA) algorithm is further proposed to solve the JSAPA and BA subproblems iteratively. Specifically, the JSAPA subproblem is solved by an extended Gale-Shapley matching algorithm and differ of convex (DC) programming method iteratively. For the BA subproblem, appropriate video bitrate is explored with subgradient method. Numerical results validate the effectiveness of the proposed algorithm and reveal that network-assisted ABS in NOMA networks benefits users in achieving higher QoE with less energy consumption, especially when radio resources are insufficient or with large segment size.