Video and Voice Transmission over LTE Networks - Semantic Scholar

2013 IEEE International Conference on Control System, Computing and Engineering, 29 Nov. - 1 Dec. 2013, Penang, Malaysia

Video and Voice Transmission over LTE Networks Nima Saed Faculty of Information Science & Technology, Multimedia University Melaka, Malaysia [email protected]

Kuokkwee WEE Faculty of Information Science & Technology, Multimedia University Melaka, Malaysia [email protected]

Tze Hui Liew Faculty of Information Science & Technology, Multimedia University Melaka, Malaysia [email protected]

Shih Yin Ooi Faculty of Information Science & Technology, Multimedia University Melaka, Malaysia [email protected] non-GBR may exists for long time since this is non-blocking resources transmission [2]. Example services for GBR are conversational voice, conversational video, real time gamming and video streaming (buffered streaming), while for non-GBR, there are TCP based services (e.g. IMs, e-mail, file sharing, etc.) [3]. Typically, LTE network consists of eNodeB, user equipment (UE), mobility management entity (MME) and gateway (GW). An example of deployment for LTE network is illustrated in Fig. 1. Point-to-multipoint (PMP) mode is the common deployment in mobile industry, whereby 1 eNodeB services multiple UEs or terminals. In a PMP mode broadband wireless access (BWA), the connections between base station and its subscriber stations are typically defined into two types; uplink and downlink.

Abstract— Video and voice transmission over wireless broadband has become popular and attracted more attention ever since. More and more hand phone owners use their phones to play video and voice over the Internet. Transmitting video and voice in a good quality over the wireless networks is a challenge to service providers. LTE, also known as one of the beyond 3G wireless network technology is designed to have a greater delivery service for multimedia, voice and video applications to end users. However, the QoS provisioning of the voice/video/multimedia in the intricacy wireless network is highly depended on the QoS framework of a network. The QoS framework for LTE network is studied and comparisons among the scheduling algorithms are made in this study. Extensive simulation results showed that the performance of the scheduling algorithms could enhance the video and voice delivery quality. Index Terms— scheduling algorithm, LTE, quality of service, 4G, wireless network.

I. INTRODUCTION The “3rd generation partnership project”, formally known as 3GPP is a standard for wireless mobile communication for high speed data on mobile devices and data terminals. Long Term Evolution (LTE) is one of the 4G standards that support high speed communication with new digital signal processing, and modulation techniques. Besides high speed data rates, LTE also provides low latency and flexible bandwidth to end users. Due to the high demand on IP base services over wireless networks, such as, video streaming, video conferencing, Voice over Internet Protocol (VoIP) and online games, the bandwidth, Quality of Service (QoS) and delay requirements must be assured and guaranteed in wireless networks. In LTE, all the applications are mapped to guaranteed and non-guaranteed bearers as described in [1]. These bearers are names as guaranteed-bit-rate (GBR) and non-guaranteed-bitrate (non-GBR) bearers. In GBR, packet lost is assumed does not occur because of overflow of buffer, but services in nonGBR may encounter packet lost issue. GBR bearers are established on demand basis since these bearers block transmission resources by reserving them. On the contrary,

978-1-4799-1508-8/13/$31.00 ©2013 IEEE

Fig. 1. LTE network deployment

Uplink transmission is referred to the connection from subscriber station to base station. Meanwhile, downlink is defined as the link from a base station to a subscriber station.

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In cellular networks, downlink is referred as the transmission path from an eNodeB (base station) to cellular phones or user equipment traffic. LTE systems use Orthogonal Frequency Domain Multiple Access (OFDMA) to provide downlink data transmission service to multiple users. In this study, the downlink is the only focus because there are typically more downloading activities which are performed by end users. End users are keen to have triple-play service, which involves the video streamlining, files downloading and web browsing. The performance of three common LTE scheduling algorithms has been tested and evaluated in 2 different scenarios in this study. The weaknesses of these algorithms have been identified, as well as future research direction for video and VOIP transmission over LTE networks. This paper is organized as follows. Section II presents the QoS architecture of LTE and Section III describes the downlink scheduling mechanisms that are used in this study. Section IV depicts the simulation experiment environment and discusses the simulation results. Lastly, conclusions and future plan are presented in Section V.

III. QOS SCHEDULING ALGORITHMS FOR VOICE AND VIDEO TRANSMISSION OVER LTE NETWORK Voice and video transmission is highly depended on the QoS framework of a medium (network). Bandwidth request [4][5], bandwidth allocation [6][7], admission control [8] and scheduling algorithms are the major components in a QoS framework. Scheduling algorithms are the most popular topic and have been discussed over the years. Scheduling algorithms for voice and video transmission over broadband wireless network, such as WiMAX and WiBro, have been extensively studied. Conventional scheduling approaches, for example strict priority [9][10], weighted round robin [11], deficit round robin [12], weighted deficit round robin [13], worst-case fair weighted fair queuing [14], earliest deadline first [15] and other packet information based scheduling approaches from [16][17][18], have been evaluated. However, the results are not convincing in LTE network. Hence, 3 types of non conventional downlink scheduling algorithms has been studied in this research. Each of the scheduling algorithms has its own advantages but all of them are aimed for real-time applications. Proportional fair scheduler (PF): Generally PF is the way to find balance between resource fairness and spectral efficiency. Users are prioritized by their channel quality relative to their average allocated rate [19]. The PF is designed based on the formula in (1) and (2).

II. QOS ARCHITECTURE OF LTE NETWORK As cellular operators across the global have seen a rapid increase in mobile broadband subscribers and also the traffic load of multimedia, video and gaming application for these advance mobile devices keep increasing. Hence, 3GPP has designed LTE with different QoS frameworks in evolved packet system (EPS). The QoS of EPS connection for the packet flow between terminal and gateway is called bearer. Meanwhile, the traffic of a terminal is separated into several service data flows (SDFs). SDFs are mapped to same bearer based on their QoS classification differently (e.g. scheduling policy, queue management policy, rate shaping policy and radio link control configuration). There are two types of bearers; default or dedicated bearer. The default bearer is nonGBR and dedicated bearer is either non-GBR or GBR [2]. The QoS attributes associated with LTE bearer are listed below [3]: QCI (QoS Class Identifier): scalable value 1 to 9, defined resource type, packet delay budget and packet error lost rate. ARP (Allocation and retention priority): parameter uses by admission control and overload control to establish/release or modify bearer when is needed. MBR (Maximum bit rate): the maximum bit rate for bearer should not exceed. It is only available for GBR bearers (in 3GPP release 8 MBR must be equal to GBR) GBR (guaranteed bite rate): the minimum traffic rate promises by a bearer. AMBR (Aggregate MBR): the total amount of bit rate of non-GBR bearers. AMBRs values can be defined separately for uplink and downlink between ARP and terminal [2]. Besides the QoS attributes, the SDFs are depended on the algorithms that schedule packet for transmission, either uplink or downlink. The scheduling algorithms play an important role to determine the sequence and the amount of packet to be delivered.

log 1

(1) (2)

,

is the expected data-rate for the -th user at where 1 is past time on the -th resource block and average throughput achieved by data flow of the -th user when scheduled. , is the metric value for PF algorithm. Modified Largest Weighted Delay First (M-LWDF): in each t time slot, it serves the j queue which the Head of Line (HOL) delay value of the j queue is maximal. This approach makes M-LWDF scheduling rule to achieve optimal , throughput [19][16]. The weight of the metric, , is calculated by (3).

log

/

(3)

is the weights of metric, is the probability where is the that packet is dropped due deadline expiration and target delay in other words refers to last time when the ith user was served. ,

where

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, , is the delay of head of line packet.

(4)


Exponential Proportional Fair (EXP/PF): It has both characteristics of PF and the delay-sensitive traffic like MLWDF but with different methods to calculate the weight. EXP/PF treats real-time and non-real-time services differently, when there is non-real-time service (e.g. Best Effort). it will calculate the metric by PF scheduling algorithm, but when there is real-time service, it calculates weight as in (5) and (6) [20][19]. ,

/

,

∑

regardless the algorithms and the speed of movement. In contrast, the video flows have significant changes as observed. There is 20% increase in PLR for EXP/PF and M-LWDF, and 65% lost for PF scheduling algorithm when the number of UEs reaches 50. With 10 UEs connected to the eNodeB, there is 8% difference between PF with others (M-LWDF and EXP/PF). When the number of UEs reaches to 50, the difference between PF with other algorithms (M-LWDF and EXP/PF) is increased to 172%. In other words, the PF is the worst among the 3 algorithms that have been evaluated. As seen in Fig. 3, the difference between UEs with speed of 3 km/h and 0 km/h, the PLR for PF with 3 km/h speed is higher than 0 km/h. However, for other algorithms (M-LWDF and EXP/PF), the average lost is higher in motionless as compared to UEs with 3 km/h speed. By comparing 0 km/h and 3 km/h, M-LWDF or EXP/PF algorithm reduces up to 8.26% in the PLR but the PF algorithm increases average lost by 8%. Fig. 4 and Fig. 5 show the average packet delays for VoIP and video flows respectively. Like the PLR performance, the VoIP delays for all scheduling algorithms are almost similar. The delay is always less than 10 milliseconds. However, the delay for M-LWDF is observed to increase slightly when the number of the UEs increased. Regarding the video flows with M-LWDF and EXP/PF scheduling algorithm, the average delay increases as the number of UEs changed. More significant observation is PF scheduling algorithm when reaching 40 and 50 UEs, its delay deviation goes higher and reaches more than 2 seconds as depicted in Fig. 5. When the number of UEs is 20, the difference between PF and MLWDF is approximately 8%. When the number of UEs reaches 30, the difference of delay for PF and M-LWDF algorithms are also increased to 340%. By the time of 50 UEs, this gap reaches up to 6150% if compared to the results at 10 UEs. In terms of the speed effect on average delays, PF scheduling algorithm decreases in the average delays, but in other scheduling algorithms (M-LWDF and EXP/PF) there are no significant changes when the speed changes from 0 to 3 km/h. Fig. 6, Fig. 7 and Fig. 8 show the average goodput for VoIP, video and best effort flows. For VoIP flows, there is a constant increment in average goodput for all scenarios and scheduling algorithms when the number of UEs increased. As observed in Fig. 6, increase in speed of UEs from 0 to 3 km/h, also increase the average goodput for PF scheduling algorithm (3.9%), but it is about 1.3% of decrement for M-LWDF and no change for EXP/PF. For video flows, there is almost constant increase in average goodput for M-LWDF and EXP/PF scheduling algorithm. At 40 UEs, there is decreasing in average goodput for PF scheduling algorithm. Clearly from Fig. 7, the best average goodput for video flows is achieved by M-LWDF and the worst case is PF. Changes in speed of the UEs from 0 to 3 km/h have negative effect on PF and positive effect on M-LWDF and EXP/PF scheduling algorithm. For instance, it is about 2.6% increase average goodput for MLWDF and EXP/PF and 7% of decrement for PF scheduling. For the best effort flows, due to the QoS-aware scheduling that gives high flow of VoIP and video, a negative effect on

(5) (6)

,

is equal to number of active downlink real time , is the delay of head of line packet for the ith user and is the weight factor for EXP that makes the difference between M-LWDF and EXP.

where flows.

IV. DISCUSSION For the performance evaluation of downlink scheduling algorithms (PF, M-LWDF and EXP/PF) in LTE, LTE-Sim simulator [21] is used to simulate the network scenario of 1 cell (eNodeB) with radius of 1 KM coverage. A number of UEs between 10 and 50 with interval of 10 are randomly surrounded the eNodeB. Two movements of UEs are simulated in this study. Firstly, the UEs are motionless (fixed) and for the second scenario, UEs are travelling with speed of 3 km/h within the coverage of the eNodeB and based on the random walk model predefined in LTE-Sim. The downlink bandwidth is configured at 10 Mhz. Other simulation parameters are as in TABLE I. There are three types of traffic that has been simulated to reflect the end users’ activities. Each UE receives 1 H.264 video flow that encoded at 128 kbps, 1 VoIP flow and 1 best effort flow with infinite buffer application. The packet loss ratio (PLR), throughput and delay are the major network performance metrics set that been evaluated in this study. TABLE I SIMULATION PARAMETERS

Simulation Parameters PHY Bandwidth Frame Structure UL/DL Frame Length Modulation Antenna Type Simulation Duration

OFDMA 10MHz TDD 10ms QAM, 4-QAM, 16-QAM Omni-directional 60s

Fig. 2 and Fig. 3 show the PLR for video flows and VoIP flows. For VoIP flows, there are no significant changes in PLR with the change in number of UEs and scheduling algorithm. The PLR for all algorithms is almost unchanged,

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the BE was detected. The average goodput decreases by increasing the number of UEs connected to eNodeB. As seen from the Fig. 8, the worst scheduling algorithm is PF and the best is M-LWDF. Changes in speed of UEs from 0 to 3 km/h have slightly increased the average goodput for all scheduling algorithm.

Fig. 5. Average packet delays for video

Fig. 2. Packet lost ratio for video

Fig. 6. Average goodput for VoIP

Fig. 3. Average packet delays for video

Fig. 7. Average goodput for video

Fig. 4. Average packet delays for VoIP

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2013 IEEE International Conference on Control System, Computing and Engineering, 29 Nov. - 1 Dec. 2013, Penang, Malaysia [8]

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[12] Fig. 8. Average goodput for best effort

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V. CONCLUSION Base on observation, M-LWDF and EXP/PF scheduling algorithm that considers about the waiting time of the packets that stay in queue, has less delay. PLR for both M-LWDF and EXP/PF are also lesser due to the short stay time in the queue. The speed of mobility harms the performance of goodput for PF but not for others. The main reason behind the results is the changes of the spectrum efficiency when UEs are moving. In conclusion, the M-LWDF has the best performances among the algorithms for video when UEs are moving with low speed.

[14] [15]

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