🤖 AI Summary
This work addresses a critical limitation in existing multimedia streaming resource allocation methods, which overlook users’ asymmetric sensitivity to throughput fluctuations—particularly their loss aversion. To bridge this gap, the study introduces prospect theory into the domain for the first time, formulating a utility model that jointly captures the average throughput and the asymmetric impact of its variations. Building upon multi-queue system theory, the authors devise a dynamic threshold-based scheduling policy that gradually reduces resource allocation as availability transitions from abundant to scarce, while preserving user priority considerations. The proposed approach enables online, rapid optimization of thresholds and demonstrates significant performance gains over conventional fair allocation schemes in simulations. Results confirm that it better aligns with real-world user satisfaction and offers strong potential for practical deployment.
📝 Abstract
Traditional resource allocation policies for multimedia streaming have primarily targeted metrics such as throughput, fairness and delay. However, user satisfaction is known to be strongly influenced by variations in throughput. Although some recent works have considered throughput variations, they overlooked a well-known aspect of human behavior that has reshaped behavioral economics over the past few decades: the same amount of decrease in throughput causes greater annoyance than the satisfaction caused by the increase. Motivated by this insight from the broad field of economics, popularly known as prospect theory, we propose a generic utility (metric) that captures both the average throughput and the asymmetric effect of throughput variations on user satisfaction. Next, we propose a resource allocation policy that reduces abrupt decreases in allocated resources when the system transitions from a resource-rich to resource-scarce state while respecting the priorities of different classes of users. Extensive simulations show that this policy outperforms the existing fair allocation policies. However, the choice of the optimal threshold depends on the network instance, which, in practice, may change frequently. Hence, building on the theoretical analysis of multi-queue systems, we propose fast numerical methods for determining the optimal threshold, which can be used in field deployments.