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
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