Ya-Ju Yu

Energy-Efficient Video Multicast in 4G Wireless Systems

Layer-based video coding, together with adaptive modulation and coding, is a promising technique for providing real-time video multicast services on heterogeneous mobile devices. With the rapid growth of data communications for emerging applications, reducing the energy consumption of mobile devices is a major challenge. This paper addresses the problem of resource allocation for video multicast in fourth-generation wireless systems, with the objective of minimizing the total energy consumption for data reception. First, we consider the problem when scalable video coding is applied. We prove that the problem is NP-hard and propose a 2-approximation algorithm to solve it. Then, we investigate the problem under multiple description coding, and show that it is also NP-hard and cannot be approximated in polynomial time with a ratio better than 2, unless P = NP. To solve this case, we develop a pseudopolynomial time 2-approximation algorithm. The results of simulations conducted to compare the proposed algorithms with a brute-force optimal algorithm and a conventional approach are very encouraging.

Decentralized energy-efficient base station operation for green cellular networks

Given the explosive growth of mobile subscribers, network operators have to densely deploy base stations to serve the exponentially increasing access demands. Nevertheless, recent researches have pointed out that base station operation has been identified as a significant portion of total system energy consumption and 90% of the traffic is carried by only 40% of base stations even under peak traffic demand. Therefore, switching off underutilized base stations for saving power is an important issue with the increasing awareness of environmental responsibility and economical concerns of network operators. This paper targets the problem of dynamic base station operation, with an objective to minimize total power consumption of all base stations. We prove this problem is NP-hard and cannot be approximated in polynomial time with a ratio better than 3 over 2. Then, we propose a distributed algorithm to tackle it. The simulation results show that our proposed algorithm can significantly reduce the network power consumption.

Mobile small cell deployment for next generation cellular networks

With the rapid growth of mobile broadband traffic, adopting small cell is a promising trend for operators to improve network capacity with low cost. However, static small cells cannot be flexibly placed to fulfill time/space-varying traffic. The static small cells might stay in idle or under-utilized mode during some time periods, which wastes resources. Therefore, this paper utilizes the mobile small cell concept and studies the deployment problem for mobile small cells. The objective is to maximize the service time provided by mobile small cells for all users. If a finite number of mobile small cells can serve more users for more time, the mobile small cell deployment will have more gains. Specifically, we show an interesting trade-off in the service time maximization. Then, we prove our target problem is NP-hard and propose an efficient mobile small cell deployment algorithm to deal with the trade-off to maximize the total service time. We construct a series of simulations with realistic parameter settings to evaluate the performance of our proposed algorithm. Compared with a static small cell deployment algorithm and a random mobile small cell deployment algorithm, the simulation results show that our proposed scheme can significantly increase the total service time provided for all users.

Utility-Based Resource Allocation for Layer-Encoded Multimedia Multicasting over Wireless Relay Networks

Layer-encoded multimedia multicasting (LMM) has emerged in recent years to support heterogeneous subscriber stations with diverse computing and networking capabilities. With the advance of broadband wireless access technologies, wireless relay networks (WRNs) are expected to provide significant improvement on throughput and extension of coverage for next-generation wireless systems. In this paper, we study a radio resource allocation problem with the consideration of wireless interferences for LMM over WRNs. The problem is formally formulated, and the objective of the problem is to maximize the system utility where the utility is defined as user satisfaction. The NP-hardness of the problem is demonstrated, and then a heuristic algorithm is proposed. A series of experiments are conducted to demonstrate the capability of our proposed algorithm.

Efficient multicast delivery for wireless data center networks

Recently, large-scale data centers are widely built to support various kinds of cloud services, which are mostly delivered by multicast. Even with multicast, cloud services may still generate a large amount of data traffic in some bottleneck links and, even worse, cause network congestion. Thus, how to reduce the redundancy of data transmissions to mitigate network congestion is essential. In addition to wired transmissions, modern data centers adopt wireless links to augment network capacity. Under the coexisting scenario of wired and wireless links, this paper studies multicast data delivery problem. Specifically, a multicast tree problem is defined, and the objective is to minimize the total multicast data traffic. We prove the problem is NP-hard and propose an efficient heuristic algorithm to solve the problem. A series of experiment results shows that our proposed algorithm is very effective, compared with an optimal solution designed for traditional wired data centers.

Ultra-low latency service provision in 5G Fog-Radio Access Networks

With the increasing demand for ultra-low latency services in 5G cellular networks, fog with edge computing is one of promising solutions which migrate the computing from the cloud to the edge of the network. Rather than relying on the distant cloud or additional servers, we propose the Fog-Radio Access Network (F-RAN), which leverages the current infrastructures in the radio access network, such as small cells and macro base stations, to pursue the ultra-low latency by joint powerful computing of multiple F-RAN nodes and near-range communications at the edge. The optimization problem is firstly formulated to tackle the tradeoff between communication and computing resources into time domain within distributed computing scenario, and then we propose a cooperative task computing operation algorithm to simultaneously decide how many F-RAN nodes should be selected with proper communication resource allocation and computing task assignment. The numerical results show that the ultra low-latency services can be achieved by F-RAN via cooperative task computing.

Adaptable packet significance determination mechanism for H.264 videos over IP dual stack networks

The video streaming applications are full of potentials in the IP dual stack network that supports IPv4 and IPv6 protocols simultaneously. However, the significances of video packets belonged to various video sequences are different. An equal error protection to all video packets in the IP network will degrade the video quality significantly. This paper proposes an Adaptive Significance Determination Mechanism in Temporal and Spatial domains (ASDM-TS) for H.264 videos over IP dual stack network with DiffServ model. ASDM-TS determines the video packet significance simultaneously in temporal and spatial domains. From the temporal domain, ASDM-TS evaluates the packet significance based on the estimated error propagation if a packet is lost. From the spatial domain, ASDM-TS computes the packet significance based on the content complexity belonging to a packet. Moreover, ASDM-TS is adaptive to various video sequences with a self-learning method. Compared with traditional schemes, simulation results show that the proposed scheme significantly improves the accuracy of signification determination up to 15% and effectively improves the received video quality up to 0.7dB in PSNR.

Profit-aware base station operation for green cellular networks

With the rapid growth of mobile data traffic, operators are expected to densely deploy base stations to meet user demands. Recent researches have indicated that the densely deployed base stations lead to the significant increase of the operational expenses of operators due to the electricity bills to maintain their operation, and thus the profit of operators is greatly decreased. Different from the past works in dynamically switching on/off base stations for energy saving, we propose to consider the benefits of users in service fee discounts as a joint optimization process in cutting down the energy consumption of base stations to maximize the total profit of operators. The optimization problem is formulated and shown being NP-hard. We then propose a profit-aware algorithm to switch off base stations, as needed, with the adjustment of the data rates provided to the users who are willing to receive discounts. The simulation results show that the proposed algorithm can significantly increase the total profit of operators and introduce a win-win situation to both users and operators.

Power-Aware Scalable Video Multicast in 4G Wireless Systems

Scalable video coding with adaptive modulation and coding is a promising technique to provide real-time multicast services for heterogeneous mobile devices. Nevertheless, as the rapid growth of data communication for emerging applications, energy consumption is an critical challenge of mobile devices. This paper targets the problem of resource allocation for scalable video multicast with adaptive modulation-coding schemes in next generation cellular wireless networks, with an objective to minimize the total energy consumption of all mobile devices for reception. We show the NP-hardness of the target problem and propose a 2-approximation algorithm. Extensive simulations are conducted to compare the proposed algorithm with a brute-force optimal algorithm and a conventional approach, which provides some useful insights into power-aware scalable video multicast in 4G wireless systems.

A resource allocation scheme for device-to-device communication over ultra-dense 5G cellular networks

With the advance of the wireless technologies and mobile devices, the number of mobile devices in cellular system will explosively grow. According to this trend, a base station will face a heavy traffic loading and even cannot provide services for a large amount of mobile devices. To deal with this issue, device-to-device (D2D) technology is a promising solution to increase spectrum efficiency by reusing radio resource blocks. In this paper, we study a resource allocation problem with the objective of maximizing system capacity over ultra-dense 5G cellular systems and consider a scenario that the number of D2D users is more than that of cellular users. This paper observes that radio resource blocks should be firstly allocated to D2D users under the ultra-dense scenario. Then, we propose resource allocation methods to solve this problem. Simulation results agree our observation and show that the proposed scheme can significantly improve the system capacity and spectrum efficiency.