This paper focuses on downlink packet scheduling for streaming video in Long Term Evolution (LTE). As a hard handover is adopted in LTE and has the period of breaking connection, it may cause a low user-perceived video quality. Therefore, we propose a handover prediction mechanism and a pre-scheduling mechanism to dynamically adjust the data rates of transmissions for providing a high quality of service (QoS) for streaming video before new connection establishment. Advantages of our method in comparison to the exponential/proportional fair (EXP/PF) scheme are shown through simulation experiments.
2. Pre-Scheduling Mechanism
2.1. Handover Prediction
where Pˆi is the predictive value of RSRP at time ti, and a and b are coefficients of the linear regression equation. Then, we use the least squares (LS) method to deduce a and b. The method of LS is a standard solution to estimate the coefficient in linear regression analysis.
where Pi is the measured value of RSRP at time ti. The least squares method is to try to find the minimum of S, and then the minimum of S is determined by calculating the partial derivatives.
where T¯¯¯= ∑ni=1tin and P¯¯¯= ∑ni=1Pin. If there are several neighbor eNodes, we select the eNodeB with the maximum variation of RSRP (maximum slope) as target eNodeB. In Figure 2a, we can see that while RSRPSeNB=RSRPTeNB, the handover procedure is triggered. We have trigger time tt=a1−a2b2−b1.
2.2. Pre-Scheduling Mechanism
where tr is the time interval from scheduling to starting handover (pre-scheduling time for handover). The starting time of scheduling is adjustable, and we will evaluate it in our simulation later. tho is the time during handover procedure. tn is the delay time before new transmission (preparation time of scheduling with new eNodeB). Ks is the required number of video frames per second and m is the number of BL that is needed in each video frame. In Figure 2b, according to transmission data rate of the serving eNodeB, we construct a linear regression line dx(t). Then, the amount of BL’s data (transmitted from serving eNodeB and stored in the buffer of users) before handover has to be no less than NBL.
where thandover is the TTT for handover. In the above inequality, the left part is the amount of data that the serving eNodeB can transmit before handover. According to the serving eNodeB capacity of transmission, we can dynamically adjust the transmission rate between BL and ELs. In Equation (6), while the inequality does not hold, it means the serving eNodeB cannot provide enough data for BL for maintaining high QoS for video streaming. Accordingly, the serving eNodeB merely transmits data for BL. On the contrary, while the inequality holds, the serving eNodeB can provide the data of BL and ELs simultaneously for desired quality of video service. In the following, we describe our mechanism of data rate adjustment between BL and ELs. The transmission rates of the BL and ELs are decreasing because the RSRP is degrading between the previous serving eNodeB and user. Hence, by the regression line dx(t), we can define the total descent rate s(slope) of transmissions as
where dBL,i and dEL,i are the transmitted number of BL and ELs during time interval ti, respectively. In Equation (9), the total transmitted number for streaming video (left part) is necessarily less than or equal to the total number of data the serving eNodeB can provide (right part). Thus, the total descent rate of transmission per tunit can be calculated as s⋅tunit. In this paper, for high QoS for video streaming, BL data has high priority for transmission. Furthermore, to achieve dynamically adjusting the transmission rate between BL and EL, we define the descent rate as
3. Performance Evaluation
3.1. The Effect of the Prediction Mechanism
3.2. Base Layer Adjustment
Conflicts of Interest
Long Term Evolution
3rd Generation Partnership Project
Maximum-Largest Weighted Delay First
scalable video coding
Reference Signal Receiving Power
physical resource blocks
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- Chang, M.J.; Abichar, Z.; Hsu, C.Y. WiMAX or LTE: Who will lead the broadband mobile Internet? IT Prof. Mag. 2010,12. [Google Scholar] [CrossRef]
- Dahlman, E.; Parkvall, S.; Skold, J.; Beming, P. 3G Evolution: HSPA and LTE for Mobile Broadband; Academic press: Burlington, MA, USA, 2010. [Google Scholar]
- Kwan, R.; Leung, C.; Zhang, J. Downlink Resource Scheduling in an LTE System; INTECH Open Access Publisher: Rijeka, Croatia, 2010. [Google Scholar]
- Proebster, M.; Mueller, C.M.; Bakker, H. Adaptive Fairness Control for a Proportional Fair LTE Scheduler. In Proceedings of the IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Instanbul, Turkey, 26–30 September 2010; pp. 1504–1509.
- Andrews, M.; Kumaran, K.; Ramanan, K.; Stolyar, A.; Whiting, P.; Vijayakumar, R. Providing quality of service over a shared wireless link. IEEE Commun. Mag. 2001, 39, 150–154. [Google Scholar] [CrossRef]
- Rhee, J.H.; Holtzman, J.M.; Kim, D.K. Scheduling of Real/Non-Real Time Services: Adaptive EXP/PF Algorithm. In Proceedings of the 57th IEEE Semiannual on Vehicular Technology Conference, Jeju, Korea, 22–25 April 2003; pp. 462–466.
- Ramli, H.A.M.; Basukala, R.; Sandrasegaran, K.; Patachaianand, R. Performance of Well Known Packet Scheduling Algorithms in the Downlink 3GPP LTE System. In Proceedings of the IEEE Malaysia International Conference on Communications (MICC), Kuala Lumpur, Malaysia, 15–17 December 2009; pp. 815–820.
- Afrin, N.; Brown, J.; Khan, J.Y. An Adaptive Buffer Based Semi-persistent Scheduling Scheme for Machine-to-Machine Communications over LTE. In Proceedings of the IEEE Eighth International Conference on Next Generation Mobile Apps, Services and Technologies (NGMAST), Oxford, UK, 10–12 September 2014; pp. 260–265.
- Patra, A.; Pauli, V.; Lang, Y. Packet Scheduling for Real-Time Communication over LTE Systems. In Proceedings of the IEEE Wireless Days (WD), Valencia, Spain, 13–15 November 2013; pp. 1–6.
- Piro, G.; Grieco, L.A.; Boggia, G.; Fortuna, R.; Camarda, P. Two-level downlink scheduling for real-time multimedia services in LTE networks. IEEE Trans. Multimed. 2011, 13, 1052–1065. [Google Scholar] [CrossRef]
- Xenakis, D.; Passas, N.; Merakos, L.; Verikoukis, C. ARCHON: An ANDSF-Assisted Energy-Efficient Vertical Handover Decision Algorithm for the Heterogeneous IEEE 802.11/LTE-Advanced Network. In Proceedings of the IEEE International Conference on Communications (ICC), Sydney, Australia, 10–14 June 2014; pp. 3166–3171.
- Xenakis, D.; Passas, N.; Verikoukis, C. A Novel Handover Decision Policy for Reducing Power Transmissions in the Two-Tier LTE Network. In Proceedings of the IEEE International Conference on the Communications (ICC), Ottawa, ON, Canada, 10–15 June 2012; pp. 1352–1356.
- Xenakis, D.; Passas, N.; Merakos, L.; Verikoukis, C. Mobility management for femtocells in LTE-advanced: Key aspects and survey of handover decision algorithms. IEEE Commun. Surv. Tutor. 2014, 16, 64–91. [Google Scholar] [CrossRef]
- Xenakis, D.; Passas, N.; Gregorio, L.D.; Verikoukis, C. A Context-Aware Vertical Handover Framework towards Energy-Efficiency. In Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC Spring), Yokohama, Japan, 15–18 May 2011; pp. 1–5.
- Xenakis, D.; Passas, N.; Merakos, L.; Verikoukis, C. Energy-Efficient and Interference-Aware Handover Decision for the LTE-Advanced Femtocell Network. In Proceedings of the IEEE International Conference on Communications (ICC), Budapest, Hungary, 9–13 June 2013; pp. 2464–2468.
- Mesodiakaki, A.; Adelantado, F.; Alonso, L.; Verikoukis, C. Energy-efficient user association in cognitive heterogeneous networks. IEEE Commun. Mag. 2014, 52, 22–29. [Google Scholar] [CrossRef]
- LTE Simulator. Available online: http://telematics.poliba.it/LTE-Sim (accessed on 12 January 2015).
- Video Trace Library. Available online: http://trace.eas.asu.edu/ (accessed on 15 February 2015).
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