Video streaming is one of the most popular Internet applications that makes up a large amount of Internet traffic. A fundamental mechanism in video streaming is adaptive bitrate (ABR) selection which decides the proper compression level for each chunk of a video to optimize the users' quality of experience (QoE). The existing ABR algorithms require significant tuning and do not generalize to diverse network conditions and personalized QoE objectives. In this paper, we propose a novel framework for meta-learning based ABR design and discuss challenges of deploying learning based ABR mechanism in real-world video streaming systems. We utilize the proposed framework to design MetaABR, a novel adaptive bitrate selection algorithm based on meta-reinforcement learning to maximize users' QoE. By jointly training multiple learning tasks with a shared meta-critic, it can provide transferrable meta-knowledge to supervise bitrate selection across tasks, and can be applied to efficiently learn a new task in unseen environment with only a few trials. We implement MetaABR on an emulation platform which connects to the Linux network protocol stack through virtual network interfaces. Extensive experiments based on real-world traces and wireless testbed show that MetaABR achieves the best comprehensive QoE compared with the state-of-the-art ABR algorithms in a variety of network environments. 

MetaABR: A Meta-Learning Approach on Adaptative Bitrate Selection for Video Streaming

Accurate and real-time traffic forecasting is of great significance for urban traffic planning, traffic control, and traffic management. However, the time-varying dynamic spatial relations and the complicated spatial– temporal dependencies are still open problems to be considered in traffic forecasting. To address these issues, we propose a graph convolutional recurrent network for traffic forecasting with a dynamic adjacency matrix within an encoder–decoder framework, named DAGCRN. The DAGCRN consists of a spatial relation extraction module (SREM), an adjacency matrix update module (AMUM), a dynamic graph convolutional recurrent module (DGCRM), and a global temporal attention module (GTAM). Specifically, SREM and AMUM are proposed to capture nodes’ mutual relations at each time step and to model the evolution of the dynamic adjacency matrix, respectively. DGCRM captures the spatial–temporal dependencies of traffic data based on dynamic graph convolution and gated recurrent unit. GTAM is designed to extract the long-range temporal dependencies between future time steps and historical time steps. Extensive experiments on two real-world traffic speed datasets demonstrate that the proposed DAGCRN outperforms a number of representative baselines consistently.

DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix

5G networks, with their notable energy consumption, pose a significant challenge. Traditional energy-saving methods, effective for 4G, struggle in heterogeneous 4G and 5G networks. In this paper, we propose the pRoactivE Data-drivEn Energy Saving Method (REDEEM) to mitigate energy consumption in heterogeneous 4G and 5G mobile networks. REDEEM spatially divides the network into meshes based on cell overlaps, predicts cell traffic for proactive control, and selects active cells within each mesh. Our framework includes energy efficiency profiling for each mesh and offloads 5G traffic onto overlapping 4G cells to reduce 5G energy usage. Experiments based on the Nanchang mobile networks validate REDEEM’s effectiveness, yielding energy savings of 3442.72 MWh over a week. Notably, our approach achieves a 53.10% energy-saving rate, surpassing threshold-based methods by 38.85%, optimization-based methods by 18.15%, and fluid capacity engine by 14.79%. It minimally impacts service quality, with less than four parts per million traffic missed. Experimental results also demonstrate REDEEM’s robustness across various temporal, spatial, and traffic load scenarios.

Mitigating Energy Consumption in Heterogeneous Mobile Networks Through Data-Driven Optimization

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization concerning with traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization concerning with traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research $R^{2}$ score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

In the intelligent transportation system, traffic forecasting, which is generally characterized as a graph spatial-temporal prediction task, plays a crucial role. It is challenging to generate reliable forecast results due to the complexity of traffic topological information and the inherent uncertainty of road traffic circumstances. Existing works generally focus on modeling spatial dependency on static graph structures, but ignore dynamic relations between road segments and cannot extract long-range traffic dependencies in spatial-temporal domains. To bridge the above gaps, we present a novel framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Multi-Hop Graph Attention Network (DMGAN). Specifically, we leverage dynamic graph modeling to capture time-varying relations across road sections and introduce the multi-hop operation in each message propagation layer to extract long-range spatial dependency. Meanwhile, we develop a fusion-attention module, preserving both local and global hidden layer outputs of the encoder, to capture both long- and short-term temporal dependencies jointly. In this way, our method can fully model complex time-varying traffic topology information and capture the internal patterns of traffic series by integrating dynamic graph structure and temporal attention component. DGMAN achieves state-of-the-art performance in three metrics, as demonstrated by experimental findings on four real-world public traffic datasets, METR-LA, PEMS-BAY, PEMS03, and PEMS07. This code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/EEHITer/2022-TKDE-DMGAN-Pytorch/tree/main</uri> for reproducibility and further studies.

DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting

With the rapid explosion of network traffic volume, the prediction of traffic overload in the cellular network, which is defined as whether the traffic of a base station exceeds a predefined threshold, has become a crucial research problem regarding energy utilization and resource allocation. Most existing methods primarily model the dynamic patterns of traffic time series and compare the results with the predefined thresholds to predict traffic overload, taking into account a large amount of small-scale redundant data. To focus on the changes near the thresholds, i.e., the traffic burst circumstances, this paper adopts the soft-attention mechanism to fuse the threshold-based discrete and continuous time series characteristics to predict traffic overload. In addition, to capture the spatial correlations of the most related neighbors, we employ a Dual-KNN mechanism to select neighboring base stations and leverage the Graph Attention Network (GAT) to capture the spatial dependencies. Furthermore, we deploy a gated Temporal Convolutional Network (TCN) to model the temporal dependencies of the network traffic. Extensive experiments demonstrate that our proposed method effectively forecasts the traffic overload and outperforms the state-of-the-art algorithms by 4.07%.

Network Traffic Overload Prediction with Temporal Graph Attention Convolutional Networks

The emerging applications including cloud AR/VR gaming, ultra-high definition (UHD) streaming, Metaverse, etc. imply the demand for ultra-high bandwidth transmission in the wide area network (WAN). During the past four decades, a great number of high-speed TCP variants have been proposed to improved the throughput for WAN transmission. This paper conducts a measurement study on loss recovery for high-speed transmission, and exposes that receive buffer starvation is a result of capability mismatch between loss recovery and high-speed TCP advancements such as non-loss-based congestion control. To mitigate this mismatch problem, an opportunistic random redundant retransmission (OR3) algorithm, as well as its TCP implementation TCP-OR3, is proposed. OR3 accelerates loss recovery by minimizing the maximum retransmission times of each packet. Experiment results shows that TCP-OR3 alleviates receive buffer starvation and tackles the bottleneck of the legacy approaches such as parallel TCP in the case of high-speed transmission.

Revisiting Loss Recovery for High-Speed Transmission

The increasingly obvious advances in live video services introduce the urgent need for enhancing network transmission performance, especially by designing an efficient congestion control (CC) scheme. Unfortunately, the previous rule-based CC methods cannot adapt well to various network conditions and statuses while machine-learning-powered CC paradigms always suffer from non-trivial system overhead and unstable effects. In this paper, we first conduct a large-scale network measurement for 800+ million live video streams, and find that QoS metrics of better-performed sessions show similarity in the same user group. We then propose AutoPlex, an inter-session multiplexing CC framework that makes full use of this similarity and automatically adjusts CC parameters (i.e., pacing rate and congestion window size). AutoPlex supports user-defined policies that can act as standards to learn QoS features of better-performed sessions. We implement the proposed AutoPlex prototype based on QUIC protocol and BBR algorithm, and conduct experiments in the real live CDN proxy. The experimental results demonstrate the potentials of AutoPlex for the transmission optimization of live video applications, in which the average (or 90th-percentile) retransmission ratio can be reduced by 24% ~ 27% (or 32% ~ 40%) while the average value of goodput/rtt is promoted by 14% ~ 32%.

AutoPlex: inter-session multiplexing congestion control for large-scale live video services

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