Procuring Spontaneous Session-Level Resource Guarantees for Real-Time Applications: An Auction Approach

Abstract

Real-time multimedia applications, such as interactive gaming, live video streaming, and augmented reality, have strict latency and bitrate requirements. However, unpredictable network conditions, such as congestion and link quality, can severely degrade the quality of experience (QoE). While buffer-based mitigations cannot be applied to real-time applications due to their immediate resource needs, recent innovations in network slicing have demonstrated the feasibility of dedicating specified amounts of network resources to individual sessions in the radio access network. Encouraged by this, we propose to reserve network resources for multimedia sessions in real time according to their declared needs, thereby providing ad hoc session-level performance guarantees . Through Wi-Fi experiments and trace-driven LTE simulations, we show that such session-level resource provisioning is robust to real-time channel fluctuations and congestion externalities over the lifetime of a session. This approach, however, raises challenges: how can the network ensure that users are honest about their resource needs and optimally allocate its limited resources to users under uncertainty in future sessions’ resource needs ? We derive a novel multi-unit combinatorial auction (MUCA) model with a unique structure that can be exploited for fast winner determination, and yet incentivize truthful bidding, properties not simultaneously achieved in a generic MUCA but essential to making real-time session guarantees. Furthermore, since dynamic bidding in real time is challenging for end-users who are budget-constrained, we develop a reinforcement learning-based utility-maximizing strategy to distribute their budget across sessions and show that it yields high user utility.