Energy-Efficient Predictive HTTP Adaptive Streaming in Mobile Cellular Networks

The rapid growth of mobile video traffic puts significant pressure on energy drain at the network as well as on the end users. Exploiting predicted channel information and designing energy-efficient content delivery protocols have recently drawn attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular HyperText Transfer Protocol (HTTP) based Adaptive Streaming (HAS) (e.g., dynamic adaptive streaming over HTTP). To this end, we develop a robust mobile edge-cloud assisted stochastic Predictive HTTP Adaptive Streaming (PHAS) optimization framework that utilizes unreliable predictions of wireless data rates in a finite look-ahead window to achieve the following objectives: first, to obtain an edge-cloud assisted framework for prediction-based HAS and identify its key functional entities and their interactions; second, to model uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach that dynamically allocates the risks and optimizes system efficiency over a time horizon; and third, to propose a heuristic algorithm allocating time slot ratio for multi-users, which improves network efficiency, fairness, and overall QoE under different prediction error variances, wireless link conditions, and buffer length constraints. Simulation studies and analytical results show that the proposed solution outperforms traditional methods in terms of average QoE, fairness, and energy efficiency.

Predictive green streaming have recently gained attention in wireless network literature due to its significant energy-savings and quality of experience (QoE) gains. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular Hypertext Transfer Protocol (HTTP) [e.g., HTTP adaptive streaming (HAS)]. To this end, we develop a stochastic predictive HTTP Adaptive streaming (PHAS) optimization framework to achieve the following objectives: 1) an edge-cloud assisted framework for prediction based HAS and identify its key functional entities and their interactions; 2) modelling uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach which dynamically allocate the risks and optimize system efficiency over a time horizon; 3) an efficient heuristic algorithm allocating time slot ratio for multi-users to improve the network efficiency, fairness and overall QoE under different prediction error variances, wireless link conditions and buffer length constraints; Simulation studies and analytical results show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.