Network Working Group X. Zhu Internet-Draft S. Mena Intended status: Informational Cisco Systems Expires: October 30, 2015 Z. Sarker Ericsson AB April 28, 2015 Modeling Video Traffic Sources for RMCAT Evaluations draft-zhu-rmcat-video-traffic-source-01 Abstract This document describes two reference video traffic source models for evaluating RMCAT candidate algorithms. The first model statistically characterizes the behavior of a live video encoder in response to changing requests on target video rate. The second model is trace- driven, and emulates the encoder output by scaling the pre-encoded video frame sizes from a widely used video test sequence. Both models are designed to strike a balance between simplicity, repeatability, and authenticity in modeling the interactions between a video traffic source and the congestion control module. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on October 30, 2015. Copyright Notice Copyright (c) 2015 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of Zhu, et al. Expires October 30, 2015 [Page 1] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Desired Behavior of Synthetic Video Traffic Model . . . . . . 3 4. Interactions Between Synthetic Video Traffic Source and Congestion Control . . . . . . . . . . . . . . . . . . . . . 4 5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6 5.1. Time-damped response to target rate update . . . . . . . 6 5.2. Temporary burst/oscillation during transient . . . . . . 6 5.3. Output rate fluctuation at steady state . . . . . . . . . 7 5.4. Rate range limit imposed by video content . . . . . . . . 7 6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 7 6.1. Choosing the video sequence and generating the traces . . 8 6.2. Using the traces in the syntethic codec . . . . . . . . . 9 6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 9 6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 11 6.3. Varying frame rate and resolution . . . . . . . . . . . . 11 7. Combining The Two Models . . . . . . . . . . . . . . . . . . 12 8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12 9. References . . . . . . . . . . . . . . . . . . . . . . . . . 12 9.1. Normative References . . . . . . . . . . . . . . . . . . 12 9.2. Informative References . . . . . . . . . . . . . . . . . 13 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14 1. Introduction When evaluating candidate congestion control algorithms designed for real-time interactive media, it is important to account for the characteristics of traffic patterns generated from a live video encoder. Unlike synthetic traffic sources that can conform perfectly to the rate changing requests from the congestion control module, a live video encoder can be sluggish in reacting to such changes. Output rate of a live video encoder also typically deviates from the target rate due to uncertainties in the encoder rate control process. Consequently, end-to-end delay and loss performance of a real-time media flow can be further impacted by rate variations introduced by the live encoder. On the other hand, evaluation results of a candidate RMCAT algorithm should mostly reflect performance of the congestion control module, Zhu, et al. Expires October 30, 2015 [Page 2] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 and somewhat decouple from pecularities of any specific video codec. It is also desirable that the evaluation tests are repeatable, and be easily duplicated across different candidate algorithms. One way to strike a balance between the above considerations is to evaluate RMCAT algorithms using a synthetic video traffic source model that captures key characteristics of the behavior of a live video encoder. To this purpose, this draft presents two reference models. The first is based on statistical modelling; the second is trace-driven. The draft also discusses the pros and cons of each approach, as well as the possibility to combine both. 2. Terminology The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described RFC2119 [RFC2119]. The terminology defined in RTP [RFC3550], RTP Profile for Audio and Video Conferences with Minimal Control [RFC3551], RTCP Extended Report (XR) [RFC3611], Extended RTP Profile for RTCP-based Feedback (RTP/AVPF) [RFC4585], Support for Reduced-Size RTCP [RFC5506], and RTP Circuit Breaker Algorithm [I-D.ietf-avtcore-rtp-circuit-breakers] apply. 3. Desired Behavior of Synthetic Video Traffic Model A live video encoder employs encoder rate control to meet a target rate by varying its encoding parameters, such as quantization step size, frame rate, and picture resolution, based on its estimate of the video content (e.g., motion and scene complexity). In practice, however, several factors prevent the output video rate from perfectly conforming to the input target rate. Due to uncertainties in the captured video scene, the output rate typically deviates from the specified target. In the presence of a significant change in target rate, it sometimes takes several frames before the encoder output rate converges to the new target. Finally, while most of the frames in a live session are encoded in predictive mode, the encoder can occasionally generate a large intra-coded frame (or a frame partially containing intra-coded blocks) in an attempt to recover from losses or re-sync with the receiver, or during the transient period of responding to target rate changes. Hence, a synthetic video source should have - o ability to change bitrate. This includes ability to change the framerate and/or the resolution, skip frames when required. Zhu, et al. Expires October 30, 2015 [Page 3] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 o ability to fluctuate around the target bitrate set by the congestion control module. o ability to add delay in convergence to the target bitrate. o ability to produce Intra-coded or repair frames on demand. While there exists many different approaches in developing a synthetic video traffic model, it is desirable that the outcome follows a few common characteristics, as outlined below. * Low Computational Complexity: The model should be computationally lightweight, otherwise it defeats the whole purpose of serving as a substitute for a live video encoder. * Temporal Pattern Similarity: The individual traffic trace instances generated by the model should mimic the temporal pattern of those from a real video encoder. * Statistical Accuracy: The synthetic traffic should match the outcome of the real video encoder in terms of statistical characteristics, such as the mean, variance, peak, and autocorrelation coefficients of the bitrate. It is also important that the statistical resemblance should hold across different time scales, ranging from tens of milliseconds to sub-seconds. * Wide Range of Coverage: The model should be easily configurable to cover a wide range of codec behaviors (e.g., with either fast or slow reaction time in live encoder rate control) and video content variations (e.g, ranging from high-motion to low- motion). These distinct behavior features can be characterized via simple statistical models, or a trace-driven approach. In the next three sections, we present an example of each. 4. Interactions Between Synthetic Video Traffic Source and Congestion Control Figure 1 illustrates how the synthetic video traffic source interacts with the congestion control module and media packet transport module at the sender. Both reference models, as described later in Section 5 and Section 6, follow the same set of interactions. We model the synthetic video encoder to take in raw video frames captured by the camera along with a set of requests from the congestion control module. It then dynamically generates a sequence Zhu, et al. Expires October 30, 2015 [Page 4] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 of encoded video frames with varying size and interval. These encoded frames are segmented and packetized into RTP packets by the RTP stack, and encapsulated over UDP/IP before they are transmitted to the network interface. Upon the receipt of an updated RTCP report from the receiver, the congestion control module may further revise its request to the synthetic video encoder, which in turn updates the size and interval of encoded video frames at its output. In our model, the key notion of "congestion control requests" --- marked as (a) in the figure --- comprises several options: o Target rate R_v(t): requested at time t from the congestion control module to the encoder. Depending on the congestion control algorithm in use, the update requests can either be periodic (e.g., once per 1 second), or on-demand (e.g., only when drastic bandwidth change over the network is observed). o Target frame rate FPS(t): the instantaneous frame rate measured in frames-per-second at time t. This depends on the native camera capture frame rate as well as the target/preferred frame rate configured by the application or user. o Instant frame skipping: the request from the congestion control module to skip the encoding of one or several captured video frames, typically when a drastic decrease in available network bandwidth is detected. o On-demand generation of intra (I) frame: the request to encode another I frame to avoid further error propagation at the receiver, if severe packet losses are observed. Strictly speaking, this request should come from the error control module, not the congestion control module in the sender. Optionally, the syntethic video encoder can inform the congestion control module of the dynamic range of its output rate for the current video contents: [R_min, R_max]. Here, R_min and R_max are meant to capture the dynamic rate range the encoder is capable of outputting. This typically depends on the video content complexity and/or display type (e.g., higher R_max for video contents with higher motion complexity, or for displays of higher resolution). Therefore, these values will not change with R_v, but may change over time if the content is changing. Zhu, et al. Expires October 30, 2015 [Page 5] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 --------------- CC requests --------------- raw video | | (a) | | RTCP frames | Synthetic | <------------- | RMCAT | report -------> | Video | | Congestion | <------- | Encoder | -------------> | Control | | | rate range | | --------------- [R_min, R_max] --------------- | encoded | video | frames | \ | / ------------------------------------------------- | RTP stack | -------> ------------------------------------------------- RTP packets Figure 1: Interaction between synthetic video encoder, congestion control, and packet transport module. 5. A Statistical Reference Model In this section, we describe one simple statistical model of the live video encoder traffic source. A more complete survey of popular methods can be found in [Tanwir2013]. 5.1. Time-damped response to target rate update While the congestion control module can update its target rate request R_v(t) at any time, our model dictates that the encoder will only react to such changes after tau_v seconds from a previous rate transition. In other words, when the encoder has reacted to a rate change request at time t, it will simply ignore all subsequent rate change requests until time t+tau_v. 5.2. Temporary burst/oscillation during transient The output rate R_o during the period [t, t+tau_v] is considered to be in transient. Based on observations from video encoder output data, we model the transient behavior of an encoder upon reacting to a new target rate request in the form of largely varying output sizes. It is assumed that the overall average output rate R_o during this period matches the target rate R_v. Consequently, the occasional burst of large frames are followed by smaller-than average encoded frames. This temporary burst is characterized by two parameters: Zhu, et al. Expires October 30, 2015 [Page 6] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 o burst duration K_d: number frames in the burst event; and o burst size K_r: ratio of a burst frame and average frame size at steady state. It can be noted that these burst parameters can also be used to mimic the insersion of a large on-demand I frame in the presence of severe packet losses. The values of K_d and K_r are fitted to reflect the typical ratio between I and P frames for a given video content. 5.3. Output rate fluctuation at steady state We model output rate R_o as randomly fluctuating around the target rate R_v after convergence. There are two variants in modeling the random fluctuation R_e = R_o - R_v: o As normal distribution: with a mean of zero and a standard deviation SIGMA specified in terms of percentage of the target rate. A typical value of SIGMA is 10 percent of target rate. o As uniform distribution bounded between -DELTA and DELTA. A typical value of DELTA is 10 percent of target rate. The distribution type (normal or uniform) and model parameters (SIGMA or DELTA) can be learned from data samples gathered from a live encoder output. 5.4. Rate range limit imposed by video content The output rate R_o is further clipped within the dynamic range [R_min, R_max], which in reality are dictated by scene and motion complexity of the captured video content. In our model, these parameters are specified by the user. 6. A Trace-Driven Model We now present the second approach to model a video traffic source. This approach is based on running an actual live video encoder offline on a set of chosen raw video sequences and using the encoder's output traces for constructing a synthetic live encoder. With this approach, the recorded video traces naturally exhibit temporal fluctuations around a given target rate request R_v(t) from the congestion control module. The following list summarizes this approach's main steps: 1) Choose one or more representative raw video sequences. Zhu, et al. Expires October 30, 2015 [Page 7] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 2) Using an actual live video encoder, encode the sequences at various bitrates. Keep just the sequences of frame sizes for each bitrate. 3) Construct a data structure that contains the output of the previous step. The data structure should allow for easy bitrate lookup. 4) Upon a target bitrate request R_v(t) from the controller, look up the closest bitrates among those previously stored. Use the frame size sequences stored for those bitrates to approximate the frame sizes to output. 5) The output of the synthetic encoder contains "encoded" frames with random contents but with realistic sizes. Section 6.1 explains steps 1), 2), and 3), Section 6.2 elaborates on steps 4) and 5). Finally, Section 6.3 briefly discusses the possibility to extend the model for supporting variable frame rate and/or variable frame resolution. 6.1. Choosing the video sequence and generating the traces The first step we need to perform is a careful choice of a set of video sequences that are representative of the use cases we want to model. Our use case here is video conferencing, so we must choose a low-motion sequence that resembles a "talking head", for instance a news broadcast or a video capture of an actual conference call. The length of the chosen video sequence is a tradeoff. If it is too long, it will be difficult to manage the data structures containing the traces we will produce in the next steps. If it is too short, there will be an obvious periodic pattern in the output frame sizes, leading to biased results when evaluating congestion controller performance. In our experience, a one-minute-long sequence is a fair tradeoff. Once we have chosen the raw video sequence, denoted S, we use a live encoder, e.g. [H264] or [HEVC] to produce a set of encoded sequences. As discussed in Section 3, a live encoder's output bitrate can be tuned by varying three input parameters, namely, quantization step size, frame rate, and picture resolution. In order to simplify the choice of these parameters for a given target rate, we assume a fixed frame rate (e.g. 25 fps) and a fixed resolution (e.g., 480p). See section 6.3 for a discussion on how to relax these assumptions. Zhu, et al. Expires October 30, 2015 [Page 8] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 Following these simplifications, we run the chosen encoder by setting a constant target bitrate at the beginning, then letting the encoder vary the quantization step size internally while encoding the input video sequence. Besides, we assume that the first frame is encoded as an I-frame and the rest are P-frames. We further assume that the encoder algorithm does not use knowledge of frames in the future so as to encode a given frame. We define R_min and R_max as the minimum and maximum bitrate at which the synthetic codec is to operate. We divide the bitrate range between R_min and R_max in n_s + 1 bitrate steps of length l = (R_max - R_min) / n_s. We then use the following simple algorithm to encode the raw video sequence. r = R_min while r <= R_max do Traces[r] = encode_sequence(S, r, e) r = r + l where function encode_sequence takes as parameters, respectively, a raw video sequence, a constant target rate, and an encoder algorithm; it returns a vector with the sizes of frames in the order they were encoded. The output vector is stored in a map structure called Traces, whose keys are bitrates and values are frame size vectors. The choice of a value for n_s is important, as it determines the number of frame size vectors stored in map Traces. The minimum value one can choose for n_s is 1, and its maximum value depends on the amount of memory available for holding map Traces. A reasonable value for n_s is one that makes the steps' length l = 200 kbps. We will further discuss step length l in the next section. 6.2. Using the traces in the syntethic codec The main idea behind the trace-based synthetic codec is that it mimics a real live codec's rate adaptation when the congestion controller updates the target rate R_v(t). It does so by switching to a different frame size vector stored in the map Traces when needed. 6.2.1. Main algorithm We maintain two variables r_current and t_current: * r_current points to one of the keys of the map Traces. Upon a change in the value of R_v(t), typically because the congestion controller detects that the network conditions have changed, r_current is updated to the greatest key in Traces that is less than Zhu, et al. Expires October 30, 2015 [Page 9] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 or equal to the new value of R_v(t). For the moment, we assume the value of R_v(t) to be clipped in the range [R_min, R_max]. r_current = r such that ( r in keys(Traces) and r <= R_v(t) and (not(exists) r' in keys(Traces) such that r < r' <= R_v(t)) ) * t_current is an index to the frame size vector stored in Traces[r_current]. It is updated every time a new frame is due. We assume all vectors stored in Traces to have the same size, denoted size_traces. The following equation governs the update of t_current: if t_current < SkipFrames then t_current = t_current + 1 else t_current = ((t_current+1-SkipFrames) % (size_traces- SkipFrames)) + SkipFrames where operator % denotes modulo, and SkipFrames is a predefined constant that denotes the number of frames to be skipped at the beginning of frame size vectors after t_current has wrapped around. The point of constant SkipFrames is avoiding the effect of periodically sending a (big) I-frame followed by several smaller- than-normal P-frames. We typically set SkipFrames to 20, although it could be set to 0 if we are interested in studying the effect of sending I-frames periodically. We initialize r_current to R_min, and t_current to 0. When a new frame is due, we need to calculate its size. There are three cases: a) R_min <= R_v(t) < Rmax: In this case we use linear interpolation of the frame sizes appearing in Traces[r_current] and Traces[r_current + l]. The interpolation is done as follows: size_lo = Traces[r_current][t_current] size_hi = Traces[r_current + l][t_current] distance_lo = ( R_v(t) - r_current ) / l framesize = size_hi * distance_lo + size_lo * (1 - distance_lo) b) R_v(t) < R_min: In this case, we scale the trace sequence with the lowest bitrate, in the following way: Zhu, et al. Expires October 30, 2015 [Page 10] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 factor = R_v(t) / R_min framesize = max(1, factor * Traces[R_min][t_current]) c) R_v(t) >= R_max: We also use scaling for this case. We use the trace sequence with the greatest bitrate: factor = R_v(t) / R_max framesize = factor * Traces[R_max][t_current] In case b), we set the minimum to 1 byte, since the value of factor can be arbitrarily close to 0. 6.2.2. Notes to the main algorithm * Reacting to changes in target bitrate. Similarly to the statistical model presented in Section 5, the trace-based synthetic codec has a time bound, tau_v, to reacting to target bitrate changes. If the codec has reacted to an update in R_v(t) at time t, it will delay any further update to R_v(t) to time t + tau_v. Note that, in any case, the value of tau_v cannot be chosen shorter than the time between frames, i.e. the inverse of the frame rate. * I-frames on demand. The synthetic codec could be extended to simulate the sending of I-frames on demand, e.g., as a reaction to losses. To implement this extension, the codec's API is augmented with a new function to request a new I-frame. Upon calling such function, t_current is reset to 0. * Variable length l of steps defined between R_min and R_max. In the main algorithm's description, the step length l is fixed. However, if the range [R_min, R_max] is very wide, it is also possible to define a set of steps with a non-constant length. The idea behind this modification is that the difference between 400 kbps and 600 kbps as bitrate is much more important than the difference between 4400 kbps and 4600 kbps. For example, one could define steps of length 200 Kbps under 1 Mbps, then length 300 kbps between 1 Mbps and 2 Mbps, 400 kbps between 2 Mbps and 3 Mbps, and so on. 6.3. Varying frame rate and resolution The trace-based synthetic codec model explained in this section is relatively simple because we have fixed the frame rate and the frame resolution. The model could be extended to have variable frame rate, variable frame resolution, or both. When the video quality for a given bitrate is low, one can decrease the frame rate (if the video sequence is currently low motion) or the frame resolution in order to attempt an improvement in the quality- Zhu, et al. Expires October 30, 2015 [Page 11] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 of-experince (QoE). On the other hand, if the bitrate increases to a point where there is no longer a perceptible improvement in the QoE, then we might afford to increase the frame resolution or the frame rate (useful if the video is currently high motion). Many techniques have been proposed to choose over time the best values for these two parameters, together with the quatization step size, in order to maximize the quality of live video codecs [Ozer2011], [Hu2010]. In the future, we will consider extending the trace-based codec to be able to use variable frame rate and/or resolution. From the perspective of congestion control performance, varying the frame resolution will not impact the outcome of a synthetic video codec: the resulting encoded video frames bear the same data size regardless of resolution choice. On the other hand, different choices of frame rates lead to different levels of burstiness in the encoded video traffic trace: e.g., many small packets at a high frame rate vs. sparsely spaced large packets at a low frame rate. Such difference in traffic profiles may affect the performance of congestion control differently, especially when outgoing packets are not paced at the transport module. We leave the investigation of varying frame rate to future work. 7. Combining The Two Models This section discusses the pros and cons of the two reference models, as well as how one may combine them for evaluation of RMCAT candidates. [TODO: Add more details to this place-holder section.] 8. IANA Considerations There are no IANA impacts in this memo. 9. References 9.1. Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, March 1997. [RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V. Jacobson, "RTP: A Transport Protocol for Real-Time Applications", STD 64, RFC 3550, July 2003. Zhu, et al. Expires October 30, 2015 [Page 12] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 [RFC3551] Schulzrinne, H. and S. Casner, "RTP Profile for Audio and Video Conferences with Minimal Control", STD 65, RFC 3551, July 2003. [RFC3611] Friedman, T., Caceres, R., and A. Clark, "RTP Control Protocol Extended Reports (RTCP XR)", RFC 3611, November 2003. [RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey, "Extended RTP Profile for Real-time Transport Control Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, July 2006. [RFC5506] Johansson, I. and M. Westerlund, "Support for Reduced-Size Real-Time Transport Control Protocol (RTCP): Opportunities and Consequences", RFC 5506, April 2009. [I-D.ietf-avtcore-rtp-circuit-breakers] Perkins, C. and V. Singh, "Multimedia Congestion Control: Circuit Breakers for Unicast RTP Sessions", draft-ietf- avtcore-rtp-circuit-breakers-05 (work in progress), February 2014. [I-D.ietf-rmcat-eval-criteria] Singh, V. and J. Ott, "Evaluating Congestion Control for Interactive Real-time Media", draft-ietf-rmcat-eval- criteria-01 (work in progress), March 2014. [I-D.ietf-rmcat-cc-requirements] Jesup, R., "Congestion Control Requirements For RMCAT", draft-ietf-rmcat-cc-requirements-04 (work in progress), April 2014. [H264] ITU-T Recommendation H.264, "Advanced video coding for generic audiovisual services", . [HEVC] ITU-T Recommendation H.265, "High efficiency video coding", . 9.2. Informative References [I-D.ietf-rtcweb-use-cases-and-requirements] Holmberg, C., Hakansson, S., and G. Eriksson, "Web Real- Time Communication Use-cases and Requirements", draft- ietf-rtcweb-use-cases-and-requirements-14 (work in progress), February 2014. Zhu, et al. Expires October 30, 2015 [Page 13] Internet-Draft Modelling Video Traffic Sources for RMCAT April 2015 [RFC5033] Floyd, S. and M. Allman, "Specifying New Congestion Control Algorithms", BCP 133, RFC 5033, August 2007. [Hu2010] Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial, Temporal and Amplitude Resolution for Rate-Constrained Video Coding and Scalable Video Adaptation", inproceedings in Proc. 19th IEEE International Conference on Image Processing (ICIP'12), September 2012. [Ozer2011] Ozer, J., "Video Compression for Flash, Apple Devices and HTML5", ISBN ISBN-13:978-0976259503, 2011. [Tanwir2013] Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic Models", journal IEEE Communications Surveys and Tutorials, vol. 15, no. 5, pp. 1778-1802., October 2013. Authors' Addresses Xiaoqing Zhu Cisco Systems 12515 Research Blvd., Building 4 Austin, TX 78759 USA Email: xiaoqzhu@cisco.com Sergio Mena de la Cruz Cisco Systems EPFL, Quartier de l'Innovation, Batiment E Ecublens, Vaud 1015 Switzerland Email: semena@cisco.com Zaheduzzaman Sarker Ericsson AB Luleae, SE 977 53 Sweden Phone: +46 10 717 37 43 Email: zaheduzzaman.sarker@ericsson.com Zhu, et al. Expires October 30, 2015 [Page 14]