Maximizing Real-Time Video QoE via Bandwidth Sharing under Markovian setting

TL;DR

The paper addresses real-time video QoE in multi-operator wireless networks by introducing bandwidth sharing across regions and a joint allocation mechanism. It formulates a stochastic optimization where QoE is a concave function of allocated timeslots and delivery must meet timely and minimum-quality constraints, with QoE maximized in the long run. An online Lyapunov-based policy using debt queues $\delta_{r,n}^{i}$ and $\sigma^{i\rightarrow j}$ decides per-period allocations $(\bm{\tau}(k),\mathbf{S}(k))$ and provably achieves $\mathcal{O}(1/V)$-suboptimality while satisfying feasibility. Empirical results show substantial QoE gains (up to 90%) from sharing, especially under arrival-rate or channel-imbalance conditions, and demonstrate robustness to coordination delays and scalability to more regions. This work provides both a solid theoretical foundation and practical insights for cross-operator bandwidth sharing to enhance real-time video QoE without additional spectrum investments, with potential extensions to incentive-based sharing mechanisms.

Abstract

We consider the problem of optimizing Quality of Experience (QoE) of clients streaming real-time video, served by networks managed by different operators that can share bandwidth with each other. The abundance of real-time video traffic is evident in the popularity of applications like video conferencing and video streaming of live events, which have increased significantly since the recent pandemic. We model the problem as a joint optimization of resource allocation for the clients and bandwidth sharing across the operators, with special attention to how the resource allocation impacts clients' perceived video quality. We propose an online policy as a solution, which involves dynamically sharing a portion of one operator's bandwidth with another operator. We provide strong theoretical optimality guarantees for the policy. We also use extensive simulations to demonstrate the policy's substantial performance improvements (of up to ninety percent), and identify insights into key system parameters (e.g., imbalance in arrival rates or channel conditions of the operators) that dictate the improvements.

Figures (9)

• Figure 1: Illustration of bandwidth sharing between operators across regions
• Figure 2: Improvement in success probability with sharing is higher for shorter deadlines ($D$)
• Figure 3: Impact of imbalance in arrival rates between two operators: (a) Percentage improvement in QoE, (b) Average number of timeslots shared per period, (c) Marginal quality
• Figure 4: Percentage improvement in QoE with variation in channel capacity.
• Figure 5: Impact of heterogeneous quality scaling factors

Theorems & Definitions (14)

• Theorem 1
• Lemma 1
• proof
• Lemma 2
• proof
• Lemma 3
• proof
• Lemma 4
• proof
• Lemma 5