Ramy Atawia

Joint Chance-Constrained Predictive Resource Allocation for Energy-Efficient Video Streaming

Predictive resource allocation (PRA) techniques that exploit knowledge of the future signal strength along roads have recently been recognized as promising approaches to save base station (BS) energy and improve user quality of service (QoS). Recent studies on human mobility patterns and wireless signal strength measurements along buses and trains have indeed supported the practical potential of PRA. An unresolved challenge, however, is modeling the uncertainty in the predictions, and developing real-time robust solutions that incorporate probabilistic QoS guarantees. This is of paramount importance in PRA due to the prediction time horizon that adds considerable complexity and increases the rate uncertainty in the problem. With these developments in mind, this paper addresses energy-efficient PRA applied to stored video streaming using chance constrained programming. The proposed solution incorporates: 1) uncertainty in predicted user rates; 2) a joint level of probabilistic constraint satisfaction over a time horizon; and 3) both optimal gradient-based and real-time guided heuristic solutions. Our framework fundamentally differs from previous PRA work in the literature where nonstochastic approaches with assumptions of perfect prediction were primarily used to demonstrate the potential energy savings and QoS gains. Numerical simulations based on a standard compliant long term evolution (LTE) system are provided to examine and compare the developed solution. Unlike existing energy-efficient PRA, the proposed framework achieves the desired QoS level under imperfect channel predictions. This robustness is attained without compromising the energy-efficiency compared to opportunistic schedulers, and thus supports PRA implementation in practice.

Robust resource allocation for predictive video streaming under channel uncertainty

Novel mobility-aware resource allocation schemes have recently been introduced for efficient transmission of stored videos. The essence of such mechanisms is to lookahead at the future rates users will experience, and then strategically buffer content into user devices when they are at peak radio conditions. For example, a user approaching poor coverage will be preallocated additional video segments to ensure smooth streaming. Advances in mobility prediction and real-time radio environment map updates are driving forces for such Predictive Video Streaming (PVS) mechanisms. Although previous efforts have demonstrated the large potential gains of PVS, ideal channel predictions were assumed. This paper addresses the problem of channel uncertainty in PVS, and proposes a robust resource allocation framework that 1) models channel uncertainty, 2) solves the PVS problem with a tunable level of quality of service guarantees, and 3) learns the degree of uncertainty, and adapts the channel model accordingly. Numerical results demonstrate the effectiveness of the proposed approach for PVS under channel variability.

Chance-constrained QoS satisfaction for predictive video streaming

The promising energy saving and QoS gains of Predictive Resource Allocation (PRA) techniques have recently been recognized in the wireless network research community. These gains were primarily introduced in light of perfect prediction of both mobility traces and anticipated channel rates. However, under real world considerations of prediction errors, the reported gains cannot be guaranteed and further investigation is needed. In this paper, we demonstrate the practical potential of PRA by developing a robust, probabilistic framework that guarantees QoS satisfaction for video streaming under imperfect predictions, without compromising the energy saving gains. The proposed PRA framework uses chance-constrained programming to model video streaming QoS for all users during the foreseen time horizon. Closed form solutions are developed using the Gaussian and Bernstein approximations based on the channel statistical measures. Extensive numerical simulations using a standard compliant Long Term Evolution (LTE) system are presented to examine the developed solutions, for different user mobility scenarios and target QoS levels. The results demonstrate the various design trade-offs involved toward the practical deployment of predictive video streaming in future generation networks.

Towards mobility-aware predictive radio access: modeling; simulation; and evaluation in LTE networks

Novel radio access techniques that leverage mobility predictions are receiving increasing interest in recent literature. The essence of these schemes is to lookahead at the future rates users will experience, and then devise long-term resource allocation strategies. For instance, a YouTube video user moving towards the cell edge can be prioritized to pre-buffer additional video content before poor coverage commences. While the potential of mobility-aware resource allocation has recently been demonstrated, several practical design aspects and evaluation approaches have not yet been addressed due to the complexity of the problem. Furthermore, since prior works have focused on specific applications there is also a strong need for a unified framework that can support different user and network requirements. For this purpose, we present a novel two-stage Predictive Radio Access Network (P-RAN) framework that can efficiently leverage both future data rate predictions in the order of tens of seconds, and instantaneous fast fading at the millisecond level. We also show how the framework can be implemented within the open source Network Simulator 3 (ns-3) LTE module, and apply it to optimize stored video delivery. A thorough set of performance tests are then conducted to assess the performance gains and investigate sensitivity to various prediction errors. Our results indicate that P-RANs can jointly improve both service quality and transmission efficiency. Additionally, we also observe that P-RAN performance can be further improved by modeling prediction uncertainty and developing robust allocation techniques.

Robust Content Delivery and Uncertainty Tracking in Predictive Wireless Networks

Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction of both mobility traces and anticipated channel rates. While the results are very promising, several technical challenges need to be overcome before PRAs can be practically adopted. Techniques that model the prediction uncertainty and provide probabilistic quality of service (QoS) guarantees are among such challenges. This differs from the traditional robust optimization of wireless resources, as PRAs use a time horizon with predicted demands and anticipated data rates. In this paper, we tackle this problem and present an energy-efficient stochastic PRAs framework that is robust to prediction uncertainty under generic error probability density functions. The framework is applied for video delivery, where the desired video demands are modeled as probabilistic chance constraints over the prediction time horizon, and a deterministic closed form is then derived based on the Bernstein approximation (BA). In addition to handling prediction uncertainty, mechanisms that track the variance of the channel in real-time are practically needed. Towards this end, we demonstrate how a particle filter (PF) can be adopted to effectively achieve this functionality. A low complexity guided heuristic algorithm is also integrated with the BA-based allocations, and particle filter (PF), to provide a real-time solution. Extensive numerical simulations using a standard compliant long term evolution system are then presented to examine the developed solutions under various operating conditions. Results indicate the ability of our framework to significantly reduce base station energy consumption while satisfying users’ QoS under practical prediction uncertainty.

Fair Robust Predictive Resource Allocation for Video Streaming under Rate Uncertainties

Predictive Resource Allocation (PRA) has demonstrated its ability to provide smooth video delivery with minimal and fair interruptions. Recent work on PRA techniques exploited rate predictions to strategically allocate the limited radio resources for delivering video content. However, existing PRA techniques assume perfect prediction of future information in order to define the maximum attainable gains. In this paper, we introduce a probabilistic robust PRA framework that handles prediction errors. By adopting chance constraint programming we were able to define a probabilistic measure on the QoS degradation due to prediction uncertainties. A deterministic non-convex formulation is then obtained using the statistical parameters of predicted rates. Accordingly, we propose a convex approximation to the formulated fair PRA, which can be solved using optimal solvers to obtain a benchmark solution for future robust PRA schemes. We evaluate non-PRA and non-robust PRA schemes considering typical error models of the predicted rates. We found these schemes to result in suboptimal fairness and increased QoS degradations with the network load. Results further reveal the ability of the introduced robust fair PRA to reach the optimal and fair QoS satisfaction levels. Our approach provides a step towards applying PRA in future wireless networks to deliver video streaming content.