Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Abstract

Aided by scalable video coding, multirate multicast has become a promising technique of providing differentiated quality of experience (QoE) for massive numbers of video subscribers operating in heterogeneous channel conditions. Nevertheless, due to the time-varying nature of wireless channels and the subscribers' diverse requirements, it is challenging to dynamically control the video rate in the light of the available radio resource to achieve the best QoE. To elaborate a little further, the time scale of resource scheduling is of short-term nature, which determines the short-term video quality variation, but from a service provider's perspective the design objective is to optimize the long-term QoE for all subscribers. Despite its importance, this problem has not been considered before. Explicitly, we formulated this problem as a time-averaged stochastic optimization problem which avoids the impact of both the shortterm channel quality fluctuation and that of the video bitrates, whilst maintaining both inter-and intra-group fairness. The stratified structure of the problem inspires us to decompose it into a two-phase optimization: coarse grained assignment for each user group and fine grained assignment for each subgroup. We propose an adaptive multicast algorithm based on Lyapunov's optimization theory for solving this problem, by striking a compelling trade-off between the system's utility and its queue stability. We quantify the achievable performance of our proposed solution based on realistic video traces.

A Survey on Video Streaming for Next-Generation Vehicular Networks

“…Two different decoding scenarios, independent decoding and joint decoding, were adopted in the algorithm design to optimize the entire system and enhance the user experience. Chen et al [213] implemented a differential QoE provision for numerous video users with time-varying and heterogeneous channel conditions in an SVC-based multicast system. By dynamically adjusting video bitrates and wireless resources, the system optimizes the long-term QoE for all users.…”

“…Multicast provides the same multimedia content to multiple users through the same frequency band. Significant improvements in channel capacity and energy efficiency will enhance the potential of multicast in V2X scenarios, where vehicles can receive multicast information from other vehicles, the internet, and roadside infrastructure [213]. As mentioned in [50], selecting a small number of vehicles as mobile network gateways allows them to obtain video content from the network via V2I communication and distribute the content among other peer vehicles through V2V communication.…”

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

“…Static resource allocation, characterized by fixed resource assignment regardless of network conditions or user demand [10], poses challenges in adapting to dynamic changes. The persistent parameters, like frequency bands or time slots, lead to underutilization during low-demand periods and congestion in high-demand situations [11].…”

The Performance Analysis of Virtual Queues for Space-Ground Integrated Networks

“…In [18], the authors introduce the Local Shortest Queue (LSQ) family of load balancing algorithms to reduce the large communication overhead due to herd behavior in such heterogeneous systems. In [19], the authors propose an adaptive multicast algorithm based on Lyapunov's optimization theory to optimize the long-term QoE for all subscribers, by striking a compelling trade-off between the system's utility and its queue stability. In [20], the authors investigate the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications by modeling the vehicles' OnBoard Units'(OBUs') buffers as single-server queuing systems.…”