Uplink Resource Allocation of multiple DASH streams for QoE-based Real and Non-Real Time Viewing over LTE

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1 th International Conference on Next Generation Mobile Applications, Security and Technologies Uplink Resource Allocation of multiple DASH streams for QoE-based Real and Non-Real Time Viewing over LTE 1 Farhan Pervez, 2 Muhammad Salman Raheel, 3 Jibran Gill, 4 Kashif Iqbal 1 R&D Dept., Saudi Telecom Company, KSA; 2 University of Wollongong, Australia; 3 NTD, Huawei Technologies, KSA; 4 Quality Assurance, ZTE, Pakistan 1 fpervez.c@stc.com.sa; 2 msr949@uowmail.edu.au; 3 jibran.gill@huawei.com; 4 kash.if.iqbal@zte.com.cn Abstract The increase in data rates provided by cellular networks and the development of mobile devices capable of recording good quality videos has resulted in increased amount of user generated content. This content can be made available live as well as on-demand for the viewers. Nowadays, most of the video traffic is rate adaptive HTTPbased knows as DASH. The paper focuses on utilizing the features of SVC in order to develop an optimized resource allocation scheme for transmitting multiple DASH video streams over LTE network in uplink direction. The physical resources are allocated to the video producers such that the QoE for both real and non-real time viewers is maximized. Simulation results are presented to demonstrate the significance of the proposed optimization on the overall user experience. Keywords DASH (Dynamic Adaptive Streaming over HTTP), SVC (Scalable Video Coding), QoE (Quality of Experience), resource allocation, live and on-demand streaming With the evolution in mobile technology, mobile services have evolved from basic voice communication to broadband multimedia services. This is due to the technology advancements achieved in terms of higher data rates, optimized resource allocation, better traffic management, reduced data latency and increased network capacity. Mobile users not only use the air interface for downloading the content but also for uploading data over the mobile network. Also, with the advent of community portals and video sharing websites, production and consumption of multimedia content has increased tremendously. It is because of excellent video recording capabilities of cameras available with mobile devices, and enhanced data rates offered by cellular networks, that the uploading of high quality multimedia content is more realistic. There is also consumer tilt towards on-demand content along with real time streaming. So the producer's uploaded content is not only of interest to live viewers but also to people who want to access the content some time in future. Moreover, services like YouTube that used to offer prerecorded videos, has also enhanced their functionality to offer live video streams to their viewers. Another example can be of social media, where people interact and share content in huge amount nowadays. Given such distribution, the popularity of live video content along with on-demand content will increase a lot in near future. This means vast increase in the user generated content (UGC) that should be available for both live and ondemand viewing. This UGC on the uplink interface adds more challenges for the network operator to deal with. According to traffic and services forecasts, mobile video traffic in 2020 will be 75% of the 30.6 exabytes generated mobile data traffic [1]. A large amount of this video traffic will be user generated. Some of the applications that are source for UGC are shown in fig. 1. I. INTRODUCTION Figure 1. Different applications contributing to UGC Nowadays most of the video traffic is HTTP-based that provides rate adaptive real time streaming [2]. Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP) standardized this HTTP-based streaming known as Dynamic Adaptive Streaming over HTTP (DASH) for mobile networks. In the near future it is expected that this type of dynamic adaptive streaming will also be a large share of the uplink traffic, with LTE, LTE-A and LTE-A pro networks providing the data rates to support it. In this paper we utilize the characteristics of H.264 based SVC in order to present a resource allocation scheme for transmitting multiple uplink DASH video streams over LTE network. The physical resources are distributed to the video producers such that the QoE for both live and on-demand viewers is maximized. Simulation results are presented to demonstrate the significance of the proposed optimization on the overall user experience. To the best of knowledge, this is the first work to provide optimum QoE jointly for both DASH real and non-real time viewers. Research work on resource allocation for uplink DASH video streams has not been much focused. For user generated content, but that only for UDP/RTP based streaming, some work has been done in [3], [4], [5] and [6]. Whereas, for DASH uplink, the work we find in [7] optimizes QoE for on-demand DASH viewing only. Mostly, downlink resource allocation has be focused for different video streams, for e.g. in [8] / /16 $31.00 $ IEEE 2016 IEEE DOI /NGMAST

2 The rest of the paper is organized as follows. In the next section, we first discuss the advantage of using H.264 based SVC for real and non-real time DASH viewing and then give the Rate-Mean Opinion Score (MOS) utility curves for DASH streams. In Section III, we present the network-centric system architecture. Section IV describes the LTE uplink model and a suitable resource allocation framework for live and ondemand viewing. Section V presents the simulation results and Section VI concludes the paper. II. ENCODING FOR MULTIPLE DASH STREAMS A. Utilizing SVC Characteristics for Uplink DASH According to 3GP-DASH specification [9], media is generated in video segments of specific duration at different bit rates at DASH server. The client then HTTP request's the generated segments of different bit rates according to its network conditions. There can be different ways of providing multiple bitrate representations of these segments that need to be send from the producer to the viewer. The most advanced method being High Efficiency Video Encoding (H.265/HEVC) which provides 40-45% of bandwidth saving in comparison to Advanced Video Coding (H.264/AVC), delivering the same video quality [10]. In both AVC and HEVC, a segment is completely and independently encoded at different bitrates. Another method is by encoding a segment in multiple layers by using either SVC extension of AVC or SVC extension of HEVC. For the later, it is still not clear whether it will find broad acceptance in the market. The SVC extension of AVC, referred as just SVC [11] here on, allows to store layers of the video as different representations which are additive to each other. This paper focuses on user generated content, so the amount of computation power required to generate DASH media segments and limited memory of the UE are two important issues. SVC dominates standard AVC in both these factors, as explained in [7], requiring less computation and storage capacity. When compared with standard HEVC, SVC requires less computation as the media is generated in one single parse. Whereas in HEVC multiple single layer streams are generated of different bit rates. Although the storage required at the UE when using HEVC can be less than SVC, the ability of the SVC layers to be additive to each other to provide better video quality would be benefited when we present our uplink resource optimization mechanism for QoEbased on-demand and live viewing in section IV. This leads to SVC being the selected scheme in our work. B. Quality Metric to judge QoE We use the Spatio-Temporal Video Quality Metric (STVQM) [12] as an example of QoE-based measure of human perception to the video quality. In multimedia applications, especially when codec is used to compress the video due to limited data rates and bandwidth, a video quality metric, like STVQM, provides a numerical indication of the perceived quality from the users' perspective of received media after compression and/or transmission. STVQM is based on PSNR, frame rate as well as spatial and temporal activity that are obtained from a video. It is a content independent metric which makes it useful for the dynamic optimization of wireless video transmission. By using STVQM, it is possible to maximize the user satisfaction by adjusting the trade-off between spatial and temporal qualities. The spatiotemporal video quality metric as presented in [12] is given in (1). The constants a and b in (1) are determined by a least-square non-linear fitting using the subjective data of the considered videos in [12], which leads to a = and b = Whereas, FR is the frame rate and SVQM is spatial video quality metric, given in (2), that takes into account spatial and temporal activity along with SPSNR. SA and TA are spatial activity and temporal activity respectively. Spatial activity is a term that refers to the number of pixels utilized in construction of a digital image. Images having higher spatial resolution are composed with a greater number of pixels than those of lower spatial resolution. Whereas, temporal activity is the number of video frames captured in a unit of time. Where the constants,, μ and s in (2) are also determined by a least square non-linear fitting using the subjective data of the considered videos, which leads to = , = 0.236, μ = 36.9, s = STVQM is expressed as a single number in the range of 0 to 100, where 0 is the lowest perceived quality, and 100 is the highest perceived quality measurement. The interpretation of the quality metric which is in the range of 0 to 100, is defined as "Rating" by ITU in [13]. Here we evaluate our results using STVQM, but we map those results onto MOS for general understanding. This is done by using the mathematical equations as defined by ITU also in [13], which explains the mapping of a 0 to 100 scale metric (i.e. Rating) to a scale metric (i.e. MOS). We decompose a DASH video stream into three SVC layers where the base layer (BL), encoded at 15 fps, provides a basic video quality and the remaining enhancement layers (EL1 and EL2), encoded at 30 fps, provide a refined video quality. Fig. 2 shows the Rate- MOS utility curves generated for different test videos we use in our simulation. These Rate-MOS utility curves are used as an application layer model for utility based resource allocation that we describe in section IV. 13

3 achievable throughput per physical resource block (PRB) for a given signal-to-noise-plus-interference ratio (SNIR). The model approximates the throughput in the uplink, after link adaptation and hybrid automatic repeat request, by an implementation loss = 0.4 compared to the Shannon capacity, as defined in (3). It further defines range of uplink parameters with SNIR min = -10dB, SNIR max = 15dB and a maximum throughput (Thr max ) of 2bps/Hz when using 16QAM and code rate of 3/4. Figure 2. Rate-MOS Utility Curves III. NETWORK-CENTRIC SYSTEM ARCHITECTIRE We consider up streaming of DASH scalable videos from video uploaders to a video portal which acts as an intermediate node for archiving of DASH video streams for live viewing and on-demand retrieval. The uplink UEs are the video producers. They have the SVC streams generated that are available for being transmitted over the radio access network. The QoE optimizer acts as a centralized resource allocation entity that decides on the resources to be provided to the video producers such that the QoE of real time and non-real time viewers is maximized. A schematic overview of the system architecture for uplink resource distribution of user generated content, from a set of video producers to a video portal, to be viewed live and on on-demand, is illustrated in fig. 3. Figure 3. System Image for Network-centric Approach IV. QoE OPTMIZED RESOURCE ALLOCATION A. LTE Uplink Model The link layer model used for LTE uplink is recommended by 3GPP in [14]. It determines the B. Resource Allocation Framework We have a limited number of different DASH video quality representations. Having only three SVC layers mean less flexibility when trying to optimize the resource distribution between the uplink video producers in order to maximize the MOS of the viewers. We use a max slope based resource allocation algorithm that provides discrete optimization which is more suitable to limited number of data rate representations. The objective function, defined in (4), determines the resource share of each user that maximizes the QoE. Subject to (4) For a given resource share, the utility function of user k is denoted by. The inequality in the constraint is due to the limited number of SVC layers, as we may end up with few resources not utilized. The max slope based resource allocation framework determines the resource share of every video producer. It first assigns resources to all video producers such that at least the base layer is uploaded on the portal for real time viewing. After that, in order to upload the enhancement layer, the algorithm then assigns the resources to the producer whose video provides the maximum gradient on the utility curve, may it be any previously not uploaded enhancement layer. These previous layers help to improvise the QoE for ondemand viewing. A deadline for the uploading of previous layers is introduced that corresponds to the time when a non-real time viewer starts watching the uploaded content from the portal. Here the gradient is defined as the increase in utility with respect to the amount of resources required for that increase. Fig. 4 shows the framework for max slope based resource allocation, where = 1 represents 100% resource. 14

4 The segment received duration (SRD) denotes a period of time from the time instant of sending a GET request for a media segment to the instant of receiving the last bit of the requested media segment. The video portal switches up the HTTP request rate if SRD is less than or equal to 1 segment, and buffered media time is larger than the predefined minimum. Whereas the switch down takes place if SRD is greater than 1 segment for two consecutive cycles. From here we state this link rate adaptation as DASH SRD. C. Simulation Results A. Scenario Figure 4. Resource Allocation Framework V. SIMULATION RESULTS The simulation model that has been implemented in our work was described in section III. Here we assume that there is no delay or congestion experienced by the packets over the LTE core network. Furthermore, we consider a single LTE cell with one enodeb, so the handover case is left out. The number of video producers uploading the video content is 8 and the number of live and on-demand viewers are 8 each. All the video producers upload different test videos that are available as DASH representations using SVC. For a summary of the parameters used, please refer to table 1. Parameters Carrier frequency System bandwidth Number of PRBs PRB size Bandwidth per PRB Channel Model Table 1. LTE Parameters Value B. Link Adaptation for DASH 2 GHz 5 MHz subcarriers 180 KHz Urban Macro cell We also implement a DASH client based rate adaptation algorithm defined in [15], which provides insight into the client s perceived quality. It is based on segment received time at the video portal. In this work, a mobile uploader act as a DASH server and the video portal as a DASH client. The resource assigning procedure at the scheduling layer can also be formulated as a binary integer linear problem, where 1 and 0 indicate whether a PRB should be assigned to the user or not. In this section we evaluate the QoE-driven slope-based resource allocation, comparing it with an optimization technique that uses binary integer linear programming. We refer this optimization as Bint-Opt. The advantage of comparing our framework with this type of optimization, over other decision making algorithms such as round robin scheduling, is that it provides the most optimized method to solve system of linear inequalities especially for applications that are modeled as resource limited. The number of optimization cycles is 50 and the total simulation time is 50 sec. Fig. 5 (a) shows the comparison between the theoretical average MOS achieved when using slopebased resource allocation and Bint-Opt, both with the same deadline of 10 sec. Although the results show that the Bint-Opt works better in terms of theoretical average MOS, but when we compare it with the amount of computation required to achieve it, in comparison to our resource allocation framework, we observe that it takes a lot of execution time. Normally, the average time taken for optimization by Bint-Opt for the complete simulation time is approximately 10 times greater. Furthermore, the execution time grows exponentially as the deadline increases or if the simulation time is much longer. Fig. 5 (b) shows the comparison between the cumulative distribution function (CDF) for mean MOS when using the slope-based resource allocation and Bint- Opt, both with 10 sec deadlines. It confirms the advantage we have in terms of average MOS when using Bint-Opt, whereas the disadvantage we mentioned in terms of execution time for it can also be confirmed by looking at the CDF plot in fig. 6. The number of scenarios used to generate the CDF is 10, where the video producers experience different channel qualities in every different scenario. The more execution time taken for optimization means that the video producers have to wait longer to upload the content on the portal which would especially effect real time DASH viewing. Therefore, due to this obvious advantage in terms of execution time, we propose our resource allocation technique over Bint-Opt. 15

5 Average MOS CDF Slope-based Resource Allocation vs Binary Integer Optimization Slope-Based deadline 10 sec Bint-Opt deadline 10 sec Optimization Cycle a) Average MOS for one single scenario Slope-based Resource Allocation vs Binary Integer Optimization Slope-Based deadline 10 sec Bint-Opt deadline 10 sec Mean MOS b) CDF for mean MOS of 10 different scenarios Figure 5. Comparison b/w QoE Slope-based algorithm and Binary Integer Optimization CDF Slope-based Resource Allocation vs Binary Integer Optimization Slope-Based deadline 10 sec Bint-Opt deadline 10 sec Mean Execution Time (sec) Figure 6. CDF for mean execution time of QoE Slope-based algorithm and Binary Integer Optimization Fig. 7 shows video quality as experienced by the DASH viewers, when the videos are uploaded using our slope-based resource allocation. We analyze the QoE of both real time i.e. live and non-real time i.e. on-demand users. The viewers try to access video streams, stored at the video portal, by the uplink producers. We assume that the on-demand viewers request for access at the end of the simulation time of 50 seconds, i.e. there are no more layers being uploaded on the video portal. Whereas, the live users begin streaming at the start of the simulation time. We only analyze the first 20 sec of the segment playout. Furthermore, for simplicity, we assume that the link between the video portal and the viewers is an ideal one. Therefore, there is no delay and network congestion experienced by any of the live or on-demand viewers. The red curve in fig. 7 (a) shows the theoretical average MOS achieved by live viewers, whereas the blue curve shows the theoretical average MOS achieved by on-demand viewers. We also analyze the SRD-based link adaptation algorithm which gives a more realistic QoE perspective of DASH viewers. The pink curve shows the average MOS of the live viewers, whereas the black one shows the average MOS of the on-demand viewers. We can see the gain in average MOS for on-demand viewers in comparison to live viewers, both theoretically and in case of a more practical SRD-based DASH link adaptation scenario. This is due to our optimized resource allocation that we perform for the uplink video producers, in which more number of higher quality layers are uploaded by the end of simulation time, resulting in better MOS for on-demand viewers. Fig. 7 (b) shows the comparison between the CDF for mean MOS for live and on-demand video consumers. It confirms the advantage we have in terms of average MOS for on-demand viewers. The gain in average MOS is 0.5 for both theoretical and in case of DASH link adaptation perspectives. The number of scenarios used to generate the CDF is still 10. Average MOS QoE Perspective of Real and Non-Real Time Viewers 4.1 Live Viewer 4.05 Live Viewer SRD On-Demand Viewer On-Demand Viewer SRD Viewing Time (sec) a) Average MOS for one single scenario 16

6 CDF QoE Perspective of Real and Non-Real Time Viewers Live Viewer 0.2 Live Viewer SRD On-Demand Viewer On-Demand Viewer SRD Mean MOS b) CDF for mean MOS of 10 different scenarios Figure 7. QoE Perspective of Real and Non-Real Time Viewers VI. CONCLUSION In this work we presented a resource allocation framework for transmitting multiple DASH video streams from mobile video producers in the uplink direction. The framework was designed such that the QoE of real time and non-real time viewers for that content is maximized. Taking into account a network centric approach, simulation results were presented for LTE uplink resource distribution, that demonstrate the significance of the proposed scheme compared to binary integer programming based optimization, and also its importance for joint optimization for enhancing QoE of live and on-demand DASH viewers. Simulation results show that the mean MOS can be improved at least by 0.5 for on-demand viewers. In future, keeping in mind the very high data rates offered by mobile networks such as LTE-A pro and supported user equipment, making it possible to deliver 4K videos, we would try to implement a simulation model for such scenario using our resource allocation framework. Furthermore, we may try to extend the QoEoptimized resource allocation by additionally considering the power consumption of the mobile devices. REFERENCES [1] Cisco, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, , White Paper Feb [2] Sandvine Intelligent Broadband Networks. Sandvine, Global Internet Phenomena Report. Technical report, Sept [3] Ali El Essaili, Eckehard Steinbach, Daniele Munaretto, Srisakul Thakolsri, and Wolfgang Kellerer. QoE-driven resource optimization for user generated video content in next generation mobile networks. In IEEE International Conference on Image Processing (ICIP 2011), Brussels, Belgium, Sep [4] Mauricio Iturralde, Steven Martin, Tara Ali Yahiya. Resource allocation by pondering parameters for uplink system in LTE Networks. In IEEE 38th Conference on Local Computer Networks (LCN), Sydney, NSW, Oct [5] M.S. Raheel, R. Raad and CRitz (2015), Achieving maximum utilization of peer's upload capacity in P2P networks using SVC, Springer Peer-to-Peer Networking and Applications Journal, pp [6] M. S. Raheel, S. Iranmanesh, R. R. Member and M. Raad, "An adaptive data collection technique for streaming video over decentralized MANETs," 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC), Beirut, 2016, pp [7] Farhan Pervez, M. S. Raheel. QoE-based network-centric resource allocation for on-demand uplink adaptive HTTP streaming over LTE network th International Conference on Application of Information and Communication Technologies (AICT). IEEE, Astana, Oct 2014, pp 1-5. [8] Farhan Pervez, M. S. Raheel. "Application-state aware scheduling for video delivery over LTE network." th International Conference on Telecommunication Systems Services and Applications (TSSA). IEEE, Bandung, 2015, pp 1-5. [9] 3GPP TS V Transparent end-to-end packetswitched streaming service (PSS); Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH) (Release 10). Technical report, [10] Grois, Dan, Detlev Marpe, Amit Mulayoff, Benaya Itzhaky, and Ofer Hadar. "Performance comparison of H.265/MPEG-HEVC, VP9, and H.264/MPEG-AVC encoders." In Picture Coding Symposium (PCS), 2013, pp IEEE, [11] Heiko Schwarz, Detlev Marpe, and Thomas Wiegand. Overview of the scalable video coding extension of the H. 264/AVC standard. Circuits and Systems for Video Technology, IEEE Transactions on, 17(9): , [12] Yang Peng and Eckehard Steinbach. A novel full-reference video quality metric and its application to wireless video transmission. In IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, Sep [13] ITU-T. The E-model: a computational model for use in transmission planning, ITU-T G.107, June [14] 3GPP TR V Evolved Universal Terrestrial Radio Access (EUTRA); Radio Frequency (RF) system scenarios. Technical report, Jun [15] Liu, C., Bouazizi, I., Hannuksela, M. M., & Gabbouj, M. Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network. Signal Processing: Image Communication, pages , Apr