Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Abstract

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks. Index Terms-Green mobile edge computing (GMEC), DASH, Quality of experience (QoE), Fairness, Greedy-based algorithm. Antti Ylä-Jääski received the Ph.D. degree from ETH Zurich in 1993. From 1994 to 2009, he was with Nokia in several research and research management positions, with a focus on future Internet, mobile networks, applications, services, and service architectures. Since 2004, he has been a tenured Professor with the Department of Computer Science, Aalto University. His current research interests include mobile cloud computing, mobile multimedia systems, pervasive computing and communications, indoor positioning and navigation, energy efficient communications and computing, and Internet of Things.

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

“…Renewable sources such as solar or wind help to move towards environmentallyfriendly video processing and streaming. In this context, interesting resource allocation problems emerge that require to balance the trade-off between the QoE of streaming, backhaul traffic, and energy consumed [268]. Second, energy consumption can be reduced by switching off under-utilized edge servers.…”

A Review on Green Caching Strategies for Next Generation Communication Networks

“…Several studies [11], [209]- [211] have proven that trafficoffloading at the green BSs not only improves energy efficiency but also ensures faster content delivery. In this regard, Zhou et al [209] reduced the number of requests handled by the MBSs.…”

“…By utilizing contextaware caching and content delivery, these SBSs reduced the burden on the MBSs and improved network coverage probability. Another cooperative technique [211] jointly exploited intermittent green energy utilization and MEC. The proposed model optimized both backhaul traffic and bit rate for the dynamic adaptation of VoD services.…”

“…Optimal content transmission mechanism [209], MDP and Iterative algorithm [11], and Stochastic geometry, Laplace function, Gamma approximation [210]. [211] Cooperative caching scheme utilizing renewable energy and MEC.…”

“…A well-designed caching strategy substantially reduces traffic flow and reduces energy demands [11], [96], [209]. Backhaul traffic is also evaluated to compare the network load and performance gain of the systems [211].…”