Rebuffering Optimization for DASH via Pricing and EEG-Based QoE Modeling

Pricing is an effective mechanism for network resource allocation that can be used to achieve a desirable balance between efficiency and fairness. However, sophisticated utility models are needed to guarantee the performance of price-based resource allocation, especially with regard to video transmission. Among various performance indices of video transmission, rebuffering is an important one that influences user quality of experience (QoE). Therefore, a price-based bandwidth allocation scheme for a dynamic adaptive streaming over hypertext transfer protocol (DASH) system is proposed for mitigating the effect of rebuffering on QoE. The utility model of the proposed scheme considers the relationship between the allocated bandwidth and the rebuffering length, as well as the effect of rebuffering length on QoE. Specifically, electroencephalography (EEG) experiments are conducted, and the distribution of the subjects' time limits at which rebuffering arouse negative emotions is used for calibration. Based on this model, the DASH server collects the buffer state information of all DASH clients periodically to adjust its bandwidth allocation. Assuming the server protects itself from congestion by pricing the clients' requested bandwidth, a Stackelberg game is formulated to study the joint utility maximization on the revenue of the server and the utility of the clients. The Stackelberg equilibrium of the game is characterized, and an efficient searching algorithm is proposed for its solution. EEG experiments are repeated on another group of subjects to verify the generalization ability of the results, and simulation results are presented to validate the effectiveness of the proposed algorithm. The proposed algorithm is shown to have low complexity and outperforms both traditional price-based scheme and QoE maximized scheme in terms of rebuffering.