HAS utilizes the HTTP protocol for streaming video content and inherits the advantages of HTTP such as transparent caching and network address translation (NAT) traversal, while the underlying transport control protocol (TCP) over which HTTP objects are transported offers congestion control functionality [1].
HAS is adaptive in the sense that the quality of the video is adjusted based on the bandwidth or data rate available between the server and the client. This is a particularly useful feature for a wireless environment since the data rate of the wireless link can vary substantially over time because of physical mobility or time-varying channel impairments such as shadowing or multipath fading, and variations in other traffic served by the same base station [1].
References
[1] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
Tuesday, August 30, 2016
Sunday, August 28, 2016
Mixed Integer Nonlinear Programming
Mixed integer nonlinear programming (MINLP) refers to optimization problems with continuous and discrete variables and nonlinear functions in the objective function and/or the constraints.
In [2] such a problem has solved for HTTP adaptive streaming over LTE networks.
References
[1] http://www.neos-guide.org/content/mixed-integer-nonlinear-programming
[2] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
In [2] such a problem has solved for HTTP adaptive streaming over LTE networks.
References
[1] http://www.neos-guide.org/content/mixed-integer-nonlinear-programming
[2] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
DASHEncoder: DASH content generation tool
DASHEncoder is open source an available at: https://github.com/slederer/DASHEncoder
References
[1] Stefan Lederer, Christopher Müller and Christian Timmerer, “Dynamic Adaptive Streaming over HTTP Dataset”, In Proceedings of the ACM Multimedia Systems Conference 2012, Chapel Hill, North Carolina, February 22-24, 2012.
References
[1] Stefan Lederer, Christopher Müller and Christian Timmerer, “Dynamic Adaptive Streaming over HTTP Dataset”, In Proceedings of the ACM Multimedia Systems Conference 2012, Chapel Hill, North Carolina, February 22-24, 2012.
How to estimate achievable rate for each UE in LTE?
If gamma is the average signal-to-noise ratio (SNR) experienced by UE, the average rate per unit bandwidth is estimated as log2(1 + gamma) [1][2][3].
In fact, it is a simplified air interface model where the achievable rate for each UE is estimated according to the average channel state information (CSI) of its link.
References
[1] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
[2] A. E. Essaili, D. Schroeder, D. Staehle, M. Shehada, W. Kellerer, and E. Steinbach, “Quality-of-experience driven adaptive HTTP media delivery,” in Proc. IEEE Int. Conf. on Commun. (ICC), Budapest, Hungary, Jun 2013.
[3] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
In fact, it is a simplified air interface model where the achievable rate for each UE is estimated according to the average channel state information (CSI) of its link.
References
[1] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
[2] A. E. Essaili, D. Schroeder, D. Staehle, M. Shehada, W. Kellerer, and E. Steinbach, “Quality-of-experience driven adaptive HTTP media delivery,” in Proc. IEEE Int. Conf. on Commun. (ICC), Budapest, Hungary, Jun 2013.
[3] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
Quality Class Indicators (QCI)
"LTE supports different types of services including web browsing, video streaming, VoIP, online gaming, real-time video, etc., with standardized quality class indicators (QCI) [1]. Each QCI defines a set of requirements for quality of service (QoS) bearers, e.g., maximum tolerable delay, packet loss rate and/or guaranteed bit-rate (GBR). A GBR bearer allows to define a minimum bit-rate and a maximum bit-rate (MBR) to be allocated to a particular UE." an excerpt from [2].
Best Effort Scenario: All UEs are non-GBR with QCI equal to 9.
According to [1]:
QoS class identifier (QCI): A scalar that is used as a reference to a specific packet forwarding behaviour (e.g. packet loss rate, packet delay budget) to be provided to a Service Data Flow (SDF). This may be implemented in the access network by the QCI referencing node specific parameters that control packet forwarding treatment (e.g. scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.), that have been pre-configured by the operator at a specific node(s) (e.g. eNodeB).
Services using a GBR QCI and sending at a rate smaller than or equal to GBR can in general assume that congestion related packet drops will not occur, and 98 percent of the packets shall not experience a delay exceeding the QCI's PDB.
Services using a Non-GBR QCI should be prepared to experience congestion related packet drops, and 98 percent of the packets that have not been dropped due to congestion should not experience a delay exceeding the QCI's PDB.
The Packet Error Loss Rate (PELR) defines an upper bound for the rate of SDUs (e.g. IP packets) that have been processed by the sender of a link layer protocol (e.g. RLC in E-UTRAN) but that are not successfully delivered by the corresponding receiver to the upper layer (e.g. PDCP in E-UTRAN). Thus, the PELR defines an upper bound for a rate of non congestion related packet losses.
In general, the rate of congestion related packet drops can not be controlled precisely for Non-GBR traffic. This rate is mainly determined by the current Non-GBR traffic load, the UE's current radio channel quality, and the configuration of user plane packet processing functions (e.g. scheduling, queue management, and rate shaping).
An operator would choose GBR QCIs for services where the preferred user experience is "service blocking over service dropping", i.e. rather block a service request than risk degraded performance of an already admitted service request.
References
[1] 3GPP, “Policy and charging control architecture,” TS 23.203, v10.7.0, 2012.
[2] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
Best Effort Scenario: All UEs are non-GBR with QCI equal to 9.
According to [1]:
QoS class identifier (QCI): A scalar that is used as a reference to a specific packet forwarding behaviour (e.g. packet loss rate, packet delay budget) to be provided to a Service Data Flow (SDF). This may be implemented in the access network by the QCI referencing node specific parameters that control packet forwarding treatment (e.g. scheduling weights, admission thresholds, queue management thresholds, link layer protocol configuration, etc.), that have been pre-configured by the operator at a specific node(s) (e.g. eNodeB).
Services using a GBR QCI and sending at a rate smaller than or equal to GBR can in general assume that congestion related packet drops will not occur, and 98 percent of the packets shall not experience a delay exceeding the QCI's PDB.
Services using a Non-GBR QCI should be prepared to experience congestion related packet drops, and 98 percent of the packets that have not been dropped due to congestion should not experience a delay exceeding the QCI's PDB.
The Packet Error Loss Rate (PELR) defines an upper bound for the rate of SDUs (e.g. IP packets) that have been processed by the sender of a link layer protocol (e.g. RLC in E-UTRAN) but that are not successfully delivered by the corresponding receiver to the upper layer (e.g. PDCP in E-UTRAN). Thus, the PELR defines an upper bound for a rate of non congestion related packet losses.
In general, the rate of congestion related packet drops can not be controlled precisely for Non-GBR traffic. This rate is mainly determined by the current Non-GBR traffic load, the UE's current radio channel quality, and the configuration of user plane packet processing functions (e.g. scheduling, queue management, and rate shaping).
An operator would choose GBR QCIs for services where the preferred user experience is "service blocking over service dropping", i.e. rather block a service request than risk degraded performance of an already admitted service request.
References
[1] 3GPP, “Policy and charging control architecture,” TS 23.203, v10.7.0, 2012.
[2] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
Comprehensive review of the MPEG DASH standard
A comprehensive review of the MPEG DASH standard for multimedia streaming over the internet can be found in [1].
References
[1] I. Sodagar, “The MPEG-DASH standard for multimedia streaming over the internet,” IEEE MultiMedia, vol. 18, no. 4, pp. 62–67, April 2011.
References
[1] I. Sodagar, “The MPEG-DASH standard for multimedia streaming over the internet,” IEEE MultiMedia, vol. 18, no. 4, pp. 62–67, April 2011.
Adaptive Video Streaming Over LTE Networks
- Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network [1]
- The proposed algorithm can be used into two modes: client-side and network-assisted.
- Authors have considered three elements to assign the rates
- Client Buffer Size (DASH standard allows clients to report it)
- Channel Condition
- Video Complexity
- Experiments have been done in "ns2".
- In their simulations, the available resources dedicated to streaming users is dynamically updated.
- They have used SSIM as for the quality metric.
- Optimization of HTTP adaptive streaming over mobile cellular networks [2]
- They have considered data and video users at the same time.
- They have used the same utility function for data and video users, however with different parameters.
- All fairness utility functions are of the same form [3].
- They didn't change the default proportionally fair scheduler inside eNB. They instructed that scheduler by providing it with GBR values.
- They compared their work with Best Effort and traditional GBR schedulers.
- All these schedulers share the same principle, described in [4].
References
[1] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).
[2] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
[3] M. Uchida, J. Kurose, “An Information-Theoretic Characterization of Weighted α-Proportional Fairness,” In Proceedings of IEEE INFOCOM'09, ( pp. 1053-1061)
[4] M. Andrews, L. Qian, A. Stlyar, “Optimal Utility Based Multi-user Throughput Allocation subject to Throughput Constraints,” In Proceedings of IEEE INFOCOM'05, Vol. 4, pp. 2415-2424, 2005
Friday, August 19, 2016
How to generate depth information
The free-viewpoint videos are captured using a camera array which capture the scene from multiple viewpoints. To generate the depth information, an additional depth camera may be provided for each viewpoint of the camera array. Alternatively, depth maps can be generated at a later time using one of the known depth estimation methods [1].
References
[1] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1-3):7–42, 2002.
References
[1] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1-3):7–42, 2002.
Virtual View Distortion Models
You can find virtual view distortion models in these papers:
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
[2] Hamza, Ahmed, and Mohamed Hefeeda. "Adaptive streaming of interactive free viewpoint videos to heterogeneous clients." Proceedings of the 7th International Conference on Multimedia Systems. ACM, 2016.
[3] T.-Y. Chung, J.-Y. Sim, and C.-S. Kim. Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding. IEEE Transactions on Image Processing, 23(8):3254–3267, August 2014.
[4] V. Velisavljevi´c, G. Cheung, and J. Chakareski. Bit allocation for multiview image compression using cubic synthesized view distortion model. In Proc. of the IEEE International Conference on Multimedia and Expo, pages 1–6, July 2011.
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
[2] Hamza, Ahmed, and Mohamed Hefeeda. "Adaptive streaming of interactive free viewpoint videos to heterogeneous clients." Proceedings of the 7th International Conference on Multimedia Systems. ACM, 2016.
[3] T.-Y. Chung, J.-Y. Sim, and C.-S. Kim. Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding. IEEE Transactions on Image Processing, 23(8):3254–3267, August 2014.
[4] V. Velisavljevi´c, G. Cheung, and J. Chakareski. Bit allocation for multiview image compression using cubic synthesized view distortion model. In Proc. of the IEEE International Conference on Multimedia and Expo, pages 1–6, July 2011.
Depth-Image-Based Rendering (DIBR)
DIBR is a technique to synthesize a non-captured view (virtual) using some of the captured (reference) views of the 3D world.
Thursday, August 18, 2016
Recommended bitrates for video streaming
Take a look at [1], [2] and [3].
References
[1] http://stackoverflow.com/questions/24198739/what-bitrate-is-used-for-each-of-the-youtube-video-qualities-360p-1080p-in
[2] https://support.google.com/youtube/answer/2853702?hl=en
[3] Mansy, Ahmed, Marwan Fayed, and Mostafa Ammar. "Network-layer fairness for adaptive video streams." IFIP Networking Conference (IFIP Networking), 2015. IEEE, 2015.
References
[1] http://stackoverflow.com/questions/24198739/what-bitrate-is-used-for-each-of-the-youtube-video-qualities-360p-1080p-in
[2] https://support.google.com/youtube/answer/2853702?hl=en
[3] Mansy, Ahmed, Marwan Fayed, and Mostafa Ammar. "Network-layer fairness for adaptive video streams." IFIP Networking Conference (IFIP Networking), 2015. IEEE, 2015.
Wednesday, August 17, 2016
Free Viewpoint Streaming
A DASH-based Free Viewpoint Video Streaming System [1]
Adaptive Streaming of Interactive Free Viewpoint Videos to Heterogeneous Clients [2]
- They have proposed a framework for adaptive free-viewpoint video streaming.
- They have developed an empirical rate-distortion model for MVD (multi-view-plus-depth) videos.
- They have developed two DIBR implementations which exploit graphics processing units (GPUs) to speed up the view synthesis process. The first implementation performs double warping view synthesis for an arbitrary camera arrangement. The second implementation performs horizontal pixel shifting for 1D parallel camera arrangements or rectified camera views. Both implementations use the OpenGL graphics API to perform the different stages of the view synthesis process.
Adaptive Streaming of Interactive Free Viewpoint Videos to Heterogeneous Clients [2]
- This is extended version of [1].
- They have predicted user's view request using a simple location estimation technique known as dead reckoning.
- They have developed a virtual view distortion model which is based on mathematical analysis. It stills needs some pre-measured rate-distortion points to find the model's parameters.
References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
[2] Hamza, Ahmed, and Mohamed Hefeeda. "Adaptive streaming of interactive free viewpoint videos to heterogeneous clients." Proceedings of the 7th International Conference on Multimedia Systems. ACM, 2016.
[2] Hamza, Ahmed, and Mohamed Hefeeda. "Adaptive streaming of interactive free viewpoint videos to heterogeneous clients." Proceedings of the 7th International Conference on Multimedia Systems. ACM, 2016.
The optimal ratio between texture and depth
According to [1], the optimal ratio between texture and depth data remains the same for any total target bit-rate.
References
[1] E. Bosc, F. Racapé, V. Jantet, P. Riou, M. Pressigout, L. Morin, and V. Jantet. A study of depth/texture bit-rate allocation in multi-view video plus depth compression. annals of telecommunications - annales des télécommunications, pages 1–11, 2013.
References
[1] E. Bosc, F. Racapé, V. Jantet, P. Riou, M. Pressigout, L. Morin, and V. Jantet. A study of depth/texture bit-rate allocation in multi-view video plus depth compression. annals of telecommunications - annales des télécommunications, pages 1–11, 2013.
How to avoid short-term bandwidth fluctuations?
In the process of estimating network bandwidth, the effect of short-term bandwidth fluctuations should be considered to avoid sudden sharp consequences.
In [1], they have used an exponential weighted moving average method.
References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
In [1], they have used an exponential weighted moving average method.
References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
DIBR for FVV
In [1], they have used Depth-Image-Based Rendering (DIBR) to synthesize a non-reference video from reference views (free-viewpoint application).
References
[1] S. Zinger, L. Do, and P. de With. Free-viewpoint depth image based rendering. Journal of Visual Communication and Image Representation, 21(5-6):533–541, 2010.
References
[1] S. Zinger, L. Do, and P. de With. Free-viewpoint depth image based rendering. Journal of Visual Communication and Image Representation, 21(5-6):533–541, 2010.
Tools to generate DASH segments
The following tools can be used to generate DASH segments out of a video source:
References
[1] GPAC multimedia framework. http://gpac.wp.mines-telecom.fr/.
- GPAC Multimedia Framework [1]
References
[1] GPAC multimedia framework. http://gpac.wp.mines-telecom.fr/.
Free-viewpoint video streaming challenges
Free-viewpoint video (FVV) streaming systems face several challenges [1]:
References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
- Responsiveness. Users should be able to interact with the system in real-time. The delay between a request for viewpoint change and the rendering of the target view should be minimized. This includes network-related delays as well as processing delays.
- Scalability. The system should be able to handle a large number of concurrent clients that are possibly viewing the scene from different angles.
- Adaptability. The system should provide the best possible quality to heterogeneous clients while handling network dynamics, such as bandwidth variation.
- Immersiveness. A user should be able to choose between a large number of viewpoints and transition smoothly between them in order to provide a truly immersive experience.
References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
LTE Transmission Modes
In LTE, multiple antenna can be used. Transmission Mode determines the way these antenna are being used. For example, what we normally call 'SISO' (Single Transmission Antenna and Single Reciever Antenna) is called 'TM1(Transmission Mode 1)'.
Transmission Mode 1
Single-antenna port, port 0
Transmission Mode 2
Transmit diversity
Transmission Mode 3
Transmit diversity if the associated rank indicator is 1, otherwise large delay CDD
Transmission Mode 4
Closed-loop spatial multiplexing
Transmission Mode 5
Mutil-user MIMO
Transmission Mode 6
Closed-loop spatial multiplexing with a single transmission layer
Transmission Mode 7
If the number of PBCH antenna ports in one, Single-antenna port, port 0; otherwise Transmit diversity
References
[1] http://www.sharetechnote.com/html/Handbook_LTE_TransmissionMode.html
Transmission Mode 1
Single-antenna port, port 0
Transmission Mode 2
Transmit diversity
Transmission Mode 3
Transmit diversity if the associated rank indicator is 1, otherwise large delay CDD
Transmission Mode 4
Closed-loop spatial multiplexing
Transmission Mode 5
Mutil-user MIMO
Transmission Mode 6
Closed-loop spatial multiplexing with a single transmission layer
Transmission Mode 7
If the number of PBCH antenna ports in one, Single-antenna port, port 0; otherwise Transmit diversity
References
[1] http://www.sharetechnote.com/html/Handbook_LTE_TransmissionMode.html
Tuesday, August 16, 2016
How to measure LTE Network Statistics on your cellphone
AZENQOS has developed an app which can be installed on android devices. This app helps you get the radio measurement parameters and layer-3 message signalling. Not all cellphones are supported yet, so you need to make sure that they support your cellphone.
References
[1] http://www.azenqos.com/
References
[1] http://www.azenqos.com/
ETTUS USRP B210 Power Settings
According to the board manufacturer:
"The receive frontends have 73 dB of available gain; and the transmit frontends have 89.5 dB of available gain. Gain settings are application specific, but it is recommended that users consider using at least half of the available gain to get reasonable dynamic range." [1]
The default values for the config files are around 125 dB for TX and 90 dB for RX. However, if you look at the log file of the eNB software, you can see that the final set values are close the ones reported by the manufacturer.
For example, look at the following lines excerpt from the eNB log:
RX Gain 0 125.000000 (49.000000) => 76.000000 (max 76.000000)
...
Actual RX gain: 76.000000...
Even though the RX gain is set to 125 in the config file, the actual RX gain is 76 dB. To access the actual values, you can take a look at "usrp_lib.cpp" file in "\targets\ARCH\USRP\USERSPACE\LIB\usrp_lib.cpp".
std::cout << boost::format("Actual TX bandwidth: %fM...") % (s->usrp->get_tx_bandwidth(i)/1e6) << std::endl;
References
[1] http://files.ettus.com/manual/page_usrp_b200.html
"The receive frontends have 73 dB of available gain; and the transmit frontends have 89.5 dB of available gain. Gain settings are application specific, but it is recommended that users consider using at least half of the available gain to get reasonable dynamic range." [1]
The default values for the config files are around 125 dB for TX and 90 dB for RX. However, if you look at the log file of the eNB software, you can see that the final set values are close the ones reported by the manufacturer.
For example, look at the following lines excerpt from the eNB log:
RX Gain 0 125.000000 (49.000000) => 76.000000 (max 76.000000)
...
Actual RX gain: 76.000000...
Even though the RX gain is set to 125 in the config file, the actual RX gain is 76 dB. To access the actual values, you can take a look at "usrp_lib.cpp" file in "\targets\ARCH\USRP\USERSPACE\LIB\usrp_lib.cpp".
std::cout << boost::format("Actual TX bandwidth: %fM...") % (s->usrp->get_tx_bandwidth(i)/1e6) << std::endl;
References
[1] http://files.ettus.com/manual/page_usrp_b200.html
Monday, August 15, 2016
CQI: Channel Quality Indicator
Basically the CQI in LTE is calculated for the downlink channel by the UE/Mobile and practically it implies:
UE does this mostly by estimating the channel quality using the downlink cell specific reference signal. The UE gets to calculate the SINR based on the strength of the reference signal and it usually has a look up table as to tell what SINR maps to what CQI. Once done, UE reports this CQI over PUCCH or PUSCH.
- Current communication channel quality
- UE wants to get the data according to current channel quality transport block size, which in turn can be directly converted into throughput
There is no explicit description in any standard on the mechanism by which the CQI is calculated, but the following factors play important roles to CQI measurement:
- signal-to-noise ratio (SNR)
- signal-to-interference plus noise ratio (SINR)
- signal-to-noise plus distortion ratio (SNDR)
UE does this mostly by estimating the channel quality using the downlink cell specific reference signal. The UE gets to calculate the SINR based on the strength of the reference signal and it usually has a look up table as to tell what SINR maps to what CQI. Once done, UE reports this CQI over PUCCH or PUSCH.
In LTE, there is a mapping between CQI and modulation scheme. With the modulation scheme in the table, you would get a certain range of MCS you can use for each CQI index. But you cannot pinpoint a specific MCS and Number of RBs. You need another condition to get the proper MCS and N RBs and it is 'Code Rate'. But still there is not a single formula that would give you a single/determined value for MCS and NRB.
The PUCCH Reporting Type determines whether Subband or Wideband CQI is being reported. [3]
The PUCCH Reporting Type determines whether Subband or Wideband CQI is being reported. [3]
References
[1] http://www.sharetechnote.com/html/Handbook_LTE_CQI.html
[2] https://www.quora.com/How-Channel-Quality-Indicator-is-calculated-in-LTE-Technology
[3] http://www.sharetechnote.com/html/Handbook_LTE_CQI_PMI_RI_ReportingConfiguation.html
[2] https://www.quora.com/How-Channel-Quality-Indicator-is-calculated-in-LTE-Technology
[3] http://www.sharetechnote.com/html/Handbook_LTE_CQI_PMI_RI_ReportingConfiguation.html
CSI: Channel State Information
CSI is a kind of collective name of several different type of indicators that UE reports:
- Channel Quality Indicator (CQI)
- Precoding Matrix Indicator (PMI)
- Precoding Type Indicator (PTI)
- Rank Indication (RI)
One of the most important purpose of CSI on UE side is to estimate the channel quality and recommend a proper precoding matrix to network.
References
[1] http://www.sharetechnote.com/html/Handbook_LTE_CSI.html
Thursday, August 11, 2016
Video Highlights in Cisco VNI Forecast and Methodology, 2015-2020 [1]
It would take an individual more than 5 million years to watch the amount of video that will cross global IP networks each month in 2020. Every second, nearly a million minutes of video content will cross the network by 2020.
Globally, IP video traffic will be 82 percent of all consumer Internet traffic by 2020, up from 70 percent in 2015. Global IP video traffic will grow threefold from 2015 to 2020, a CAGR of 26 percent. Internet video traffic will grow fourfold from 2015 to 2020, a CAGR of 31 percent.
Internet video surveillance traffic nearly doubled in 2015, from 272 petabytes (PB) per month at the end of 2014 to 516 PB per month in 2015. Internet video surveillance traffic will increase tenfold between 2015 and 2020. Globally, 3.9 percent of all Internet video traffic will be due to video surveillance in 2020, up from 1.5 percent in 2015.
Virtual reality traffic quadrupled in 2015, from 4.2 PB per month in 2014 to 17.9 PB per month in 2015. Globally, virtual reality traffic will increase 61-fold between 2015 and 2020, a CAGR of 127 percent.
Internet video to TV grew 50 percent in 2015. Internet video to TV will continue to grow at a rapid pace, increasing 3.6-fold by 2020. Internet video-to-TV traffic will be 26 percent of consumer Internet video traffic by 2020, up from 24 percent in 2015.
Consumer VoD traffic will nearly double by 2020. Ultra-high-definition (UHD) will be 20.7 percent of IP video-on-demand (VoD) traffic in 2020, up from 1.6 percent in 2015.
Content delivery network (CDN) traffic will carry nearly two-thirds of all internet video traffic by 2020. By 2020, 64 percent of all Internet video traffic will cross CDNs, up from 45 percent in 2015.
References
[1] http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
Globally, IP video traffic will be 82 percent of all consumer Internet traffic by 2020, up from 70 percent in 2015. Global IP video traffic will grow threefold from 2015 to 2020, a CAGR of 26 percent. Internet video traffic will grow fourfold from 2015 to 2020, a CAGR of 31 percent.
Internet video surveillance traffic nearly doubled in 2015, from 272 petabytes (PB) per month at the end of 2014 to 516 PB per month in 2015. Internet video surveillance traffic will increase tenfold between 2015 and 2020. Globally, 3.9 percent of all Internet video traffic will be due to video surveillance in 2020, up from 1.5 percent in 2015.
Virtual reality traffic quadrupled in 2015, from 4.2 PB per month in 2014 to 17.9 PB per month in 2015. Globally, virtual reality traffic will increase 61-fold between 2015 and 2020, a CAGR of 127 percent.
Internet video to TV grew 50 percent in 2015. Internet video to TV will continue to grow at a rapid pace, increasing 3.6-fold by 2020. Internet video-to-TV traffic will be 26 percent of consumer Internet video traffic by 2020, up from 24 percent in 2015.
Consumer VoD traffic will nearly double by 2020. Ultra-high-definition (UHD) will be 20.7 percent of IP video-on-demand (VoD) traffic in 2020, up from 1.6 percent in 2015.
Content delivery network (CDN) traffic will carry nearly two-thirds of all internet video traffic by 2020. By 2020, 64 percent of all Internet video traffic will cross CDNs, up from 45 percent in 2015.
References
[1] http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
How to Solve a Decentralized POMDP
Solving a Dec-POMDP is a really challenging task. In fact, it is known that the problem of finding the optimal solution for a finite-horizon Dec-POMDP with even only two agents is NEXP-complete [1].
Therefore, so much effort has been spent by researchers during last decade to create efficient methods for finding exact or approximate solution of Dec-POMDP. [2] provides a recent survey of the existing methods.
In [3], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming. They have used Joint Equilibrium based Search for Policies (JESP) [4] to solve their Dec-POMDP model. JESP [4] is guaranteed to find a locally optimal joint policy. It relies on a procedure called alternating maximization, that computes a maximizing policy for one agent at a time, while keeping the policies of the other agents fixed.
Multi-Agent Decision Process (MADP) Toolbox [5], which provides software tools for modeling, specifying, planning and learning a variety of decision-theoretic problems in multi-agent systems.
References
[1] D. S. Bernstein, S. Zilberstein, and N. Immerman, “The Complexity of Decentralized Control of Markov Decision Processes,” in Proc. Uncertainty in Artifical Intelligence, 2000, pp. 32–37.
[2] C. Amato, G. Chowdhary, A. Geramifard, N. K. Ure, and M. J. Kochenderfer, “Decentralized Control of Partially Observable Markov Decision Processes,” in Proc. IEEE CDC, 2013, pp. 2398–2405.
[3] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[4] R. Nair, M. Tambe, M. Yokoo, D. Pynadath, and S. Marsella, “Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings,” in Proc. IJCAI, 2003, pp. 705–711.
[5] F. A. Oliehoek, M. T. J. Spaan, and P. Robbel, “MultiAgent Decision Process (MADP) Toolbox 0.3,” 2014.
Therefore, so much effort has been spent by researchers during last decade to create efficient methods for finding exact or approximate solution of Dec-POMDP. [2] provides a recent survey of the existing methods.
In [3], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming. They have used Joint Equilibrium based Search for Policies (JESP) [4] to solve their Dec-POMDP model. JESP [4] is guaranteed to find a locally optimal joint policy. It relies on a procedure called alternating maximization, that computes a maximizing policy for one agent at a time, while keeping the policies of the other agents fixed.
Multi-Agent Decision Process (MADP) Toolbox [5], which provides software tools for modeling, specifying, planning and learning a variety of decision-theoretic problems in multi-agent systems.
References
[1] D. S. Bernstein, S. Zilberstein, and N. Immerman, “The Complexity of Decentralized Control of Markov Decision Processes,” in Proc. Uncertainty in Artifical Intelligence, 2000, pp. 32–37.
[2] C. Amato, G. Chowdhary, A. Geramifard, N. K. Ure, and M. J. Kochenderfer, “Decentralized Control of Partially Observable Markov Decision Processes,” in Proc. IEEE CDC, 2013, pp. 2398–2405.
[3] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[4] R. Nair, M. Tambe, M. Yokoo, D. Pynadath, and S. Marsella, “Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings,” in Proc. IJCAI, 2003, pp. 705–711.
[5] F. A. Oliehoek, M. T. J. Spaan, and P. Robbel, “MultiAgent Decision Process (MADP) Toolbox 0.3,” 2014.
Modeling the Internet Backbone Traffic
In [1], the probability distribution function of the cross-traffic for a backbone link has been considered as Gaussian. It is based on a study [2] on modeling the Internet backbone traffic at the flow level. The mean and variance of the rate of the cross-traffic are calculated in terms the average size and duration of the contributing flows.
References
[1] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[2] C. Barakat, P. Thiran, G. Iannaccone, C. Diot, and P. Owezarski, “Modeling Internet backbone traffic at the flow level,” IEEE Transactions on Signal Processing, vol. 51, no. 8, pp. 2111–2124, 2003.
References
[1] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[2] C. Barakat, P. Thiran, G. Iannaccone, C. Diot, and P. Owezarski, “Modeling Internet backbone traffic at the flow level,” IEEE Transactions on Signal Processing, vol. 51, no. 8, pp. 2111–2124, 2003.
MDP vs. POMDP
Markov Decision Process (MDP) [1] models decision problems under uncertainty when the full state information is available. In many real world problems this is not the case and only incomplete state information might be observable. Partially Observable Markov Decision Process (POMDP) [2] provides a powerful modeling framework for such problems. In multi-agent environments where there are several active decision-makers, Decentralized POMDP (Dec-POMDP) [3] is used.
In [4], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming.
References
[1] D. P. Bertsekas, Dynamic programming and optimal control. Athena Scientific Belmont, MA, 1995, vol. I-II.
[2] L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, “Planning and acting in partially observable stochastic domains,” Artificial Intelligence, vol. 101, no. 1-2, pp. 99–134, May 1998.
In [4], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming.
References
[1] D. P. Bertsekas, Dynamic programming and optimal control. Athena Scientific Belmont, MA, 1995, vol. I-II.
[2] L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, “Planning and acting in partially observable stochastic domains,” Artificial Intelligence, vol. 101, no. 1-2, pp. 99–134, May 1998.
[3] F. a. Oliehoek, “Decentralized POMDPs,” in Reinforcement Learning: State-of-the-Art, M. Wiering and M. V. Otterlo, Eds. Springer, 2012, pp. 471–503.
[4] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[4] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
A Unique Family of Functions for Fainess Measures
Fairness can have many different interpretations and criteria. Various fairness measures have been proposed across different scientific disciplines.
Jain’s index [1] is very popular in network resource allocation.
It has been shown in [2] that many fairness measures can be explained via a single family function f_beta(.). For example, the Jain’s index corresponds to the special case of beta = -1.
References
[1] R. K. Jain, D.-M. W. Chiu, and W. R. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System,” Tech. Rep., 1984.
[2] T. Lan, D. Kao, M. Chiang, A. Sabharwal, and M. Hiang, “An Axiomatic Theory of Fairness in Network Resource Allocation,” in Proc. IEEE INFOCOM, Mar. 2010, pp. 1–9.
Jain’s index [1] is very popular in network resource allocation.
It has been shown in [2] that many fairness measures can be explained via a single family function f_beta(.). For example, the Jain’s index corresponds to the special case of beta = -1.
References
[1] R. K. Jain, D.-M. W. Chiu, and W. R. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System,” Tech. Rep., 1984.
[2] T. Lan, D. Kao, M. Chiang, A. Sabharwal, and M. Hiang, “An Axiomatic Theory of Fairness in Network Resource Allocation,” in Proc. IEEE INFOCOM, Mar. 2010, pp. 1–9.
TCP-Friendly Rate Control (TFRC) Feedback Packets
The major source of information about network congestion level is the receiver’s estimate of packet loss rate, which is included in TCP-Friendly Rate Control (TFRC) [1] feedback packets.
References
[1] S. Floyd, M. Handley, J. Padhye, and J. Widmer, “TCP Friendly Rate
Control (TFRC): Protocol Specification (Proposed Standard),” 2008.
[Online]. Available: https://tools.ietf.org/html/rfc5348
References
[1] S. Floyd, M. Handley, J. Padhye, and J. Widmer, “TCP Friendly Rate
Control (TFRC): Protocol Specification (Proposed Standard),” 2008.
[Online]. Available: https://tools.ietf.org/html/rfc5348
Mapping QoS to QoE
ITU-T G.1070 [1] opinion model can be used to map the network’s Quality of Service (QoS) to user’s Quality of Experience (QoE).
For example, it has been used in "A Dec-POMDP Model for Congestion Avoidanceand Fair Allocation of Network Bandwidth inRate-Adaptive Video Streaming" [2] paper.
References
[1] “ITU-T Recommendation G.1070 Opinion model for video-telephony applications,” 2012.
[2] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
For example, it has been used in "A Dec-POMDP Model for Congestion Avoidanceand Fair Allocation of Network Bandwidth inRate-Adaptive Video Streaming" [2] paper.
References
[1] “ITU-T Recommendation G.1070 Opinion model for video-telephony applications,” 2012.
[2] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
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