Scheduling For Video Streaming Research Articles

Multi-agent DRL-based Multipath Scheduling for Video Streaming with QUIC

The popularization of video streaming brings challenges in satisfying diverse Quality of Service (QoS) requirements. The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, has the potential to improve video streaming performance with multiple simultaneously transmitting paths. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, while applying current multipath schedulers into MPQUIC, our experimental results show that they fail to adapt to various receive buffer sizes of different devices and comprehensive QoS requirements of video streaming. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-agent deep Reinforcement learning (MADRL)-based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and deploy in Dynamic Adaptive Streaming over HTTP system. Then, we compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.

An Exploration-driven Reinforcement Learning Model for Video Streaming Scheduling in 5G-Powered Drone

In the process of wireless network video streaming, especially in more complex scenarios (such as video transmission of 5G-powered drones), analyzing the quality of experience (QoE) of the video streaming is a very crucial task. Thus attention should be paid to the dynamic interaction between QoE indicators including buffer starvation probability and traffic load. This paper proposes a video streaming scheduling model based on reinforcement learning. By learning the correlation between user behavior and traffic patterns, a series of resource allocation strategies that optimize QoE indicators are obtained. Since there is a certain degree of randomness in the network status at each moment in the transmission process, the model introduces exploration rewards to solve the noise problem of random environments. At the same time, this mechanism enables the model to fully explore the environment even when the reward is sparse, so as to obtain an effective scheduling strategy. Simulation experiments have proved that our model can improve the long-term QoE of video streaming in different network environments.

Dynamic Soft Real-Time Scheduling with Preemption Threshold for Streaming Media

Over the last decades, the advancements in networking technology and new multimedia devices have motivated the research on efficient video streaming mechanisms under wireless. We consider combing soft real-time video streaming scheduling with threshold to minimize the ineffective preemption. Based on the value density and urgency of soft real-time task, the dynamic scheduling with preemption threshold strategy (DSPT) is proposed in the paper. By analyzing the response time and preemption relationship of tasks, the preemption thresholds are assigned. Simulation results show that the DSPT strategy achieves improvements about success rate, delay time, and benefit of the system.

Resource allocation and user association for HTTP adaptive streaming in heterogeneous cellular networks with small cells

Video streaming, especially hypertext transfer protocol based (HTTP) adaptive streaming (HAS) of video, has been expected to be a dominant application over mobile networks in the near future, which brings huge challenge for the mobile networks. Although some works have been done for video streaming delivery in heterogeneous cellular networks, most of them focus on the video streaming scheduling or the caching strategy design. The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored. In this paper, the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied, we model the optimization problem as a mixed integer programming problem. And to reduce the computational complexity, an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved. Then we use the many-to-one matching model to analyze the user association problem, and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Distortion and Deadline Aware Packet Scheduling for Video Streaming over Wireless

Distortion and Deadline Aware Packet Scheduling for Video Streaming over Wireless

Minimum Delay Broadcast Scheduling for Video Streaming with QoS Constraints in Wireless Mesh Networks

Minimum Delay Broadcast Scheduling for Video Streaming with QoS Constraints in Wireless Mesh Networks

Full-sharing: efficient bandwidth scheduling for video streaming over broadband cable networks (BCNs)

Broadband Cable Networks (BCNs) bring high-speed Internet access to home and make emerging multimedia streaming applications feasible. However, bandwidth contention is still a challenging problem in providing efficient IP-based Video-On-Demand (VOD) service on BCNs, due to the lack of effective approaches to exploit the unique characteristics of BCNs. To address the bandwidth contention issue, we propose an efficient video scheduling technique, called full-sharing scheduling in this paper. This technique fully exploits the unique characteristics of BCNs to reduce the bandwidth consumption of video sessions sharing a cable channel of fixed capacity, thereby maximizing the number of simultaneous video sessions on the single channel. Furthermore, we analyze the expected bandwidth and the session blocking probability of a video under the full-sharing scheduling. Based on this analysis, we design an efficient video assignment mechanism for maximizing the profit of a VOD system in scheduling videos on BCNs. Through both analysis and simulation, we show that our approach minimizes the bandwidth consumption of video sessions compared with the previous approaches and has significant advantages on BCNs. The proposed approach is also directly applicable on other broadcast/multicast networks in which clients have sufficient buffer and downstream bandwidth, e.g., satellite broadband networks.