17-10-2016, 09:36 AM
Multisource video steaming in wireless networks using resource allocation and scheduling
1459316264-Multisourcevideosteaminginwirelessnetworksusingresourceallocationandscheduling.docx (Size: 20.29 KB / Downloads: 5)
Abstract—Multi-user video streaming over wireless channels with multiple helper nodes is a challenging problem, where the demand for better video quality ,better transmission rate, queue stability needs to be reconciled with the limited and often varying communication resources with time.The Solutions to the above problem may gives to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment .This system uses a heuristic distributed protocol to find a joint routing and spectrum allocation for a single VoD session request that minimizes the total bandwidth cost in the network while satisfying the constraints. DOFDM -based cognitive radios are more powerful and flexible than traditional radios, which enable the access of larger amount of spectrum and more flexibility in channel assignment.
INTRODUCTION
The concept behind Multi source Video streaming in Wireless Networks using resource allocation and scheduling is first the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers, then dynamic adaptation of resources.
In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment.
To solve Network Utility Maximization issue, i) dynamic adaptive video quality and helper selection at user ii) dynamically allocate the helper to user transmission rates at helper node.
DOFDM used to aggregate the non-continuous carriers and having On/OFF subcarrier information key is using to activate and deactivate the subcarrier .
Cognitive radio (CR) techniques have been proposed to efficiently use the spectrum through an adaptive, dynamic, and intelligent process Spectrum utilization can be improved by permitting a secondary user (who is not being serviced) to access a spectrum hole unoccupied by the primary user, or to share the spectrum with the primary user under certain interference constraints. CR refers to di fferent approaches to this problem that seek to overlay, underlay, or interweav e the secondary user’s signals with those of the primary users. In the underlay settings, cognitive users can communicate as long as the interference caused to non cognitive users is below a certain threshold. Overlay systems, on the contrary , adopts a less conservative policy by permitting cognitive and non cognitive users to communicate simultaneously exploiting side information and using sophisticated coding techniques . Perhaps the most conservative of the three, is the interweav e system that permits to cognitive users to communicate provided that the actual spectrum is unoccupied by non cognitive users. More details on these three systems can be found, for instance, in From an information-theoretical point-ofview, establishing performance limits of these systems relies strongly on the available side information that a cognitive user has about the network nodes: channel state information (CSI), coding techniques, codebooks
In existing system used heuristic algorithms for finding the joint routing and channel assignment that maximizes the throughput for the flows. It is fixed channel approach, that is, they treat each primary user’s frequency band as a channel, and each cognitive radio can be assigned only one channel in the channel assignment. The optimal spectrum assignment for multihop flows in cognitive radio networks is more complex than the case of single-hop flows
In this paper, we studied the multisource video on-demand streaming in cognitive wireless mesh networks. We propose a heuristic distributed protocol to find a joint routing and spectrum allocation for a single VoD session request that minimizes the total bandwidth cost in the network while satisfying the constraints. We propose a centralized algorithm, which has the promise of finding better solutions. The centralized algorithm runs on the
receiver.
System
System Methodology
Related Works
A.Downlink Scheduling and Resource Allocation for OFDM Systems
We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer tone allocations, different subchannelization schemes, maximum SNR constraint per tone, and “self-noise” due to channel estimation errors and phase noise.During each time-slot a subset of users must be scheduled, and the available tones and transmission power must be allocated among them. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using a dual formulation, we give an optimal algorithm for this problem when multiple users can time-share each tone. We then give several low complexity heuristics that enforce integer tone allocations. Simulations are used to compare the performance of different algorithms.
B. Radio Resource Management Radio resource management is the system level control of co-channel interference and other radio transmission characteristics in communication system, for example cellular networks, wireless networks and broadcasting systems. The objective, utilize the limited radio spectrum resources and radio network infrastructure efficiently. Multi-user and multi-cell network capacity issues other than point-to-point capacity of each and every channel contributed radio resource management. The cost for deploying a wireless network is normally dominated by base station sites (real estate costs, planning, maintenance, distribution network, energy, etc.) and sometimes also by frequency license fees. The objective of radio resource management is therefore typically to maximize the throughput and system spectral effectively and efficiently in bit/s/Hz/base station site or Erlang/MHz/site, under constraint that the grade of service should be above a certain level. Traditional telecommunications research and education often dwell upon channel coding and source coding. The latter involves covering a certain area and avoiding outage due to all the co-channel interference, noise, attenuation caused by long distances, fading caused due to shadowing along with multipath Doppler shift and other forms of distortion. The grade of service is also affected by blocking due to the strategic admission control, scheduling starvation or inability to g admission control, scheduling starvation or inability to guarantee quality of service that is requested by the users.
C.Adaptive Resource Allocation in OFDM Systems Using GA and Fuzzy Rule Base System
Adaptive resource allocation is one of the hottest topic in almost every field of study and research now a day. It promises optimal utilization of resources while satisfying certain number of constraints. A similar constrained optimization problem has been solved for OFDM environment where channel hostilities are mitigated and throughput is maximized by adaptively selecting code rate, modulation symbol and transmits power. Many adaptive bit and power loading techniques have been investigated in the literature for enhancement of transmission rate in combination with Orthogonal Frequency Division Multiplexing (OFDM).
In these systems mainly adaptive coding modulation or adaptive power was the focus but not both. In this paper, two new schemes are proposed to adapt code rate, modulation size as well as transmit power based upon channel conditions and quality of service demand by any subcarrier. Adaptive coding and modulation is done by using a Fuzzy Rule Base System (FRBS) to enhance the achievable data rate in an OFDM system with a fixed
target bit error rate and fixed transmit power for each subcarrier. Moreover, for power adaptation two approaches are proposed, first the conventional water-filling algorithm and in second technique Genetic Algorithm is used to choose the optimum power vector. Both of these schemes are tuned in conjunction with FRBS. Moreover, the value of constant K for water-filling algorithm is calculated analytically. Simulation results show that water-filling performs algorithm better than flat power distribution while Genetic Algorithm assisted adaptive power outperforms both fixed and water-filling assisted adaptive power.
D.Ergodic Capacity of Cognitive Radio under Imperfect Channel State Information
A spectrum-sharing communication system where the secondary user is aware of the instantaneous channel state information (CSI) of the secondary link, but knows only the statistics and an estimated version of the secondary transmitterprimary receiver (ST-PR) link, is investigated. The optimum power profile and the ergodic capacity of the secondary link are derived for general fading channels (with continuous probability density function) under average and peak transmit-power constraints and with respect to two different interference constraints: an interference outage constraint and a signal-to-interference outage constraint. When applied to Rayleigh fading channels, our results show, for instance, that the interference constraint is harmful at high-power regime in the sense that the capacity does not increase with the power, whereas at low-power regime, it has a marginal impact and no-interference performance corresponding to the ergodic capacity under average or peak transmit power constraint in absence of the primary user, may be achieved
E.Fair subcarrier and power allocation for multiuser orthogonal frequency-division multiple access cognitive radio networks using a Colonel Blotto game
• The problem of subcarrier allocation (SA) and power allocation (PA) for both the downlink and uplink of cognitive radio networks (CRNs) is studied. Two joint SA and PA schemes based on Blotto games are presented for orthogonal frequency-division multiple access (OFDMA)-based CRNs. In this work, the authors consider a more practical scenario by taking into account the correlation between adjacent subcarriers. In the proposed games, secondary users (SUs) simultaneously compete for subcarriers using a limited budget. In order to win as many good subcarriers as possible, the SUs are required to wisely allocate their budget subject to the transmit power, budget and interference temperature constraints.Two PA and budget allocation strategies are derived to enable fair sharing of spectrum among the SUs. It is shown that by manipulating the total budget available for each SU, competitive fairness can be enforced. In addition, the conditions to ensure the existence and uniqueness of Nash equilibrium (NE) in the proposed methods are established and algorithms which ensure convergence to NE are proposed. Simulation results show that the proposed methods can converge rapidly and allocate resources fairly and efficiently in correlated fading OFDMA channels.
Proposed Algorithm:
Bucket Leaky Algorithm
Bucket Leaky Algorithm is an algorithm used to check that data transmissions, in the form of packets, conform to defined limits on bandwidth.
A description of the concept of operation of the Bucket Leaky Algorithm as a meter that can be used in either traffic policing or traffic shaping, may be stated as follows:
A fixed capacity bucket, associated with each virtual connection or user, leaks at a fixed rate.
If the bucket is empty, it stops leaking.
For a packet to conform, it has to be possible to add a specific amount of water to the bucket: The specific amount added by a conforming packet can be the same for all packets, or can be proportional to the length of the packet.
If this amount of water would cause the bucket to exceed its capacity then the packet does not conform and the water in the bucket is left unchanged.
Rate Allocation Algorithm:
We consider rate allocation algorithm for resolving fundamental problem of bandwidth allocation among flows in a packet-switched network. The classical max-min rate allocation has been widely regarded as a fair rate allocation policy. But, for a flow with a minimum rate requirement and a peak rate constraint, the classical max-min policy no longer suffices to determine rate allocation since it is not capable of supporting either the minimum rate or the peak rate constraint from a flow. We generalize the theory of the classical max-min rate allocation with the support of both the minimum rate and peak rate constraints for each flow. Additionally, to achieve generalized max-min rate allocation in a fully distributed packet network.
The challenge of bandwidth sharing and rate allocation in a lambda network is how to efficiently and fairly share the capacity of each source and sink among active sessions. Of course, the allocation algorithm should also be stable. We describe the main bandwidth sharing objectives as follows. First, the rate allocation (bandwidth sharing) algorithm should efficiently utilize of the capacity of each source and sink while maintaining feasibility.
Advantages:
Adaptability to network congestions
Smooth adaptation to network dynamism
The ability to handle measurements inaccuracies
Supporting multicast