WO2017007474A1

WO2017007474A1 - Congestion-aware anticipatory adaptive video streaming

Info

Publication number: WO2017007474A1
Application number: PCT/US2015/039611
Authority: WO
Inventors: Salam Akoum; Joydeep Acharya
Original assignee: Hitachi, Ltd.
Priority date: 2015-07-08
Filing date: 2015-07-08
Publication date: 2017-01-12

Abstract

Example implementations of the present disclosure are directed to systems and method for anticipatory video streaming for mobile users for congestion control in cellular networks. The example implementations involve a video streaming system and method as well as a scheduling algorithm to maintain Quality of Experience (QoE) at the video users.

Description

CONGESTION-AWARE ANTICIPATORY ADAPTIVE VIDEO STREAMING BACKGROUND Field [0001] The present disclosure relates to streaming systems in wireless networks, and more specifically, to adaptive video streaming systems. Related Art [0002] In related art systems, adaptive hypertext transfer protocol (HTTP) based video streaming is becoming mainstream in media streaming applications. Related art systems further employ video standards that can facilitate the use of adaptive streaming. The implementation of adaptive streaming in the related art breaks up the media file into segments of equal length which can be encoded at different resolutions and/or bitrates. The client fully controls the streaming media on a per-segment basis. [0003] To maintain continuous playback, the related art video streaming client maintains a video buffer to absorb the temporary mismatch between the video download rate and the video playback rate. The video buffer is important to maintain a desired Quality of Experience (QoE) for the user, as the user moves from one enhanced node B (eNB) coverage area to the next eNB coverage area, or when the UE experiences changes in channel quality or cellular load conditions. [0004] FIG. 1 illustrates a scenario for the radio access network (RAN) and evolved packet core (EPC) components of a related art long term evolution (LTE) network. The EPC facilitates the connection from the RAN to the internet. EPC can include elements such as Policy and Charging Enforcement Function (PCEF), Policy and Charging Rules Function (PCRF), Mobility Management Entity (MME), Packet Data Network Gateway (P-GW), and Serving Gateway (S-GW). Such elements can be implemented in hardware, or a combination of hardware and software. The S-GW routes and forwards user data packets, and may also serve as a mobility anchor for the user plane during handovers. The P-GW is configured to conduct policy enforcement, packet filtering for each user, and packet screening functions. [0005] In the event of a mobile user UE 102 being handed over from one source eNB 100 to a target eNB 101, because of mobility or radio channel conditions degradation, no flow control is implemented at the source eNB 100 before the handover. The data is forwarded from source eNB 100 until the connection is established between UE 102 and target eNB 101, as illustrated in FIG. 2. If the target eNB 101 is congested, and the UE 102 cannot be allocated enough wireless resources, the video buffer risks underrunning, thereby causing a degradation of the video QoE of UE 102. In the case of congestion at target eNB 101, the PCEF may be performing flow control to bring down the downlink data rate as shown in FIG. 1 in the core network. The degree of flow rate reduction for the connected UEs however is based solely on their Quality of Service (QoS) classes and current congestion level. However, the flow control does not take into account the incoming UEs or their RAN conditions and priority classes. [0006] FIG.2 illustrates an example flow diagram for a handover operation (HO) between source and target eNBs. Related art implementations of techniques for optimizing adaptive video streaming to improve the end-to-end data rate, or improve the resource allocations take into account standard approaches to optimize the delivery of segments, or prefetching the media content, or optimizing the wireless resources to improve the quality of experience of the users. SUMMARY [0007] In example implementations disclosed herein, there are systems and methods to combine a variable segment size with application layer and physical layer scheduling optimization to improve the QoE of mobile video users, which does not occur in related art implementations. [0008] Example implementations of the present disclosure illustrate an approach for adaptive streaming and resource allocation method for mobile video traffic. Delivery of video content over mobile broadband networks is expected to be the prevailing traffic in mobile networks. The rapid growth of video traffic volume is straining the resources of the mobile networks, resulting in congestion. Increasing the wireless resources or offloading to other networks such as wireless local area networks (WLAN) may not be always a feasible and reliable option for network operators, thus it can be desirable to reduce traffic volume and increase network efficiency by leveraging the ability to forecast the user mobility pattern and the user throughput to improve the resource allocation and reduce cell congestion. [0009] Cellular network predictability and user mobility allows network operators to schedule traffic when capacity is abundant, so that congestion is reduced. This can be done in an anticipatory mode, before the user reaches an overloaded base station, or in a predictive mode at the target congested base station. Optimization of the traffic scheduler may improve the performance of the mobile cellular network. [0010] In example implementations, there are systems and methods to adapt video streaming to alleviate congestion and improve user quality of experience (QoE). The example implementations of the present disclosure utilize prediction of the user mobility behavior and the class of the user, as well as prediction of the traffic patterns and the channel quality in the geographical area that the user traverses moving from a coverage area of one source base station to the next target base station. [0011] In example implementations, there is a determination, from the traffic and user mobility prediction module, the level of RAN congestion at the base stations in the geographical area of interest. It then selects target eNBs and target UEs for optimization of the video streaming. In example implementations, there is a modification to the adaptive video streaming method such that the size of the video segments is variable depending on the congestion level prediction. Further, in example implementations there is an optimization of application layer scheduling to anticipate buffer underruns depending on user characteristics. In example implementations, there is a cross layer scheduling to prioritize users according to their QoS class, or velocity, such that overall QoE can be improved. Example implementations also involve optimized flow control across base stations such that anticipatory flow control at the source base station can be traded off with predictive flow control at the target base station to impact (e.g., improve) the overall network performance. [0012] By use of example implementations, the RAN load balancing takes into account the end-to-end application requirements. Example implementations facilitate a method by which RAN level information such as existing base station load and channel conditions can be combined with application information to perform application aware RAN load balancing. Such example implementations may improve the end-to-end quality of service (QoS) of the all users in the cellular network. [0013] Aspects of the present disclosure include an apparatus, which can involve a memory, configured to store user equipment (UE) information regarding a mobility pattern of a plurality of UEs associated with a first base station, and predicted traffic congestion at the first base station and a second base station; and a processor, configured to, based on the predicted traffic congestion, determine whether the second base station is congested or predicted to be congested within a time period. For a determination that the second base station is congested or predicted to be congested within the time period the processor can be configured to determine moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on the mobility pattern; determine video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on downlink traffic; and apply flow control on video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. [0014] Aspects of the present disclosure further include a method, which can include managing user equipment (UE) information regarding a mobility pattern of a plurality of UEs associated with a first base station, predicted traffic congestion at the first base station and a second base station, and a radio access network (RAN) traffic report associated with the second base station; based on the predicted traffic congestion and the RAN traffic report associated with the second base station, determining whether the second base station is congested or predicted to be congested within a time period. For a determination that the second base station is congested or predicted to be congested within the time period, determining moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on the mobility pattern; determining video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on downlink traffic; and applying flow control on video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. The method may be implemented in the form of a computer program having instructions stored on a non-transitory computer readable medium for executing a process. [0015] Aspects of the present disclosure further include, an apparatus, which can involve a memory, configured to store user equipment (UE) information regarding buffer status of a UE that is to undergo handover from a first base station to a second base station, and a variably sized segment flag associated with UE; and a processor, configured to, for the variably sized segment flag indicating provision of variably sized segments for the UE, provide variably sized segments to the UE. BRIEF DESCRIPTION OF DRAWINGS [0016] FIG. 1 illustrates a scenario for the radio access network (RAN) and evolved packet core (EPC) components of a related art long term evolution (LTE) network. [0017] FIG. 2 illustrates an example flow diagram for a handover operation(HO) between source and target eNBs. [0018] FIG.3 illustrates a flow control utilizing traffic prediction module and UE mobility prediction module, in accordance with an example implementation. [0019] FIG. 4 illustrates the signaling flow utilized for implementing the flow control mechanism in an LTE network in accordance with an example implementation. [0020] FIG. 5 shows the operation at the video server or the content data network (CDN) cache, in accordance with an example implementation. [0021] FIG. 6 illustrates the hardware components of the video server, in accordance with an example implementation. [0022] FIG. 7 shows the detailed function diagram of the flow control mechanism, in accordance with an example implementation. [0023] FIG.8 illustrates example hardware configurations for a PCEF, in accordance with an example implementation. [0024] FIG. 9 illustrates a flow diagram of an example implementation for an example flow control mechanism where both the base stations (source eNB, and target eNB) are involved in the congestion control process, in accordance with an example implementation. [0025] FIG. 10 shows the flowchart diagram of the neighboring base station case, in accordance with an example implementation. [0026] FIG. 11 illustrates an example computing environment with an example computer device suitable for use in some example implementations. [0027] FIG. 12 illustrates an example base station upon which example implementations can be implemented. [0028] FIG. 13 illustrates an example user equipment upon which example implementations can be implemented. DETAILED DESCRIPTION [0029] The following detailed description provides further details of the figures and example implementations of the present application. Reference numerals and descriptions of redundant elements between figures are omitted for clarity. Terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term “automatic” may involve fully automatic or semi-automatic implementations involving user or administrator control over certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing implementations of the present application. The terms enhanced node B (eNB), small cell (SC), base station (BS) and pico cell may be utilized interchangeably throughout the example implementations. The terms traffic and data may also be utilized interchangeably throughout the example implementations. The implementations described herein are also not intended to be limiting, and can be implemented in various ways, depending on the desired implementation. [0030] In example implementations, there is a utilization of the UE mobility pattern forecasting and the UE-specific RAN awareness while deciding anticipated flow control needed to ensure smooth video playback at mobile video users. Example implementations can apply to non-live streaming. [0031] FIG. 3 illustrates a flow control utilizing a traffic prediction module and a UE mobility prediction module, in accordance with an example implementation. As illustrated in FIG. 3, example implementations utilize traffic history patterns to predict traffic in the immediate future (e.g., a time period as set by the operator) at source and target eNBs in a particular geographical area. The traffic history patterns, along with the UE mobility patterns and parameters, can be used to decide on application flow rate control for various UEs at both target and source eNBs. In FIG. 3, a traffic prediction module 300 collects the traffic history pattern from the traffic history database 301 and uses the traffic history pattern with the current traffic conditions to predict traffic in the future. A UE RAN information database 302 also collects historical mobility patterns of mobile UEs and mobility parameters such as UE velocity, and uses the patterns and parameters with current mobility information in the UE mobility prediction module to predict location of the UEs in the future. This information is utilized by the PCEF while deciding upon the location and the degree of flow control needed for each UE to guarantee congestion-free operation at both source and target eNBs. Traffic prediction module 300 may be implemented as described, for example, in PCT Application No. PCT/US2015/012750, filed on January 23, 2015, the contents of which are herein incorporated by reference in its entirety in all purposes. One or more video servers 303 may interact with the EPC and the RAN to provide video content to the EPC through the internet, which is ultimately sent to the UEs through the RAN. The one or more video servers 303 may also be implemented in the form of a content data network cache to store videos from the internet that are commonly downloaded as determined by the EPC. Further details of the video server are provided in the description with respect to FIG.5 and FIG.6. [0032] In example implementations, the user mobility patterns can be inferred from the historical network information of the UEs. For example, from the reporting from the UE to the base station of the velocity, and other mobility parameters, the trajectory of the UE can be predicted by methods known in the art. Mobility patterns can also be inferred from statistical models used in cellular networks, as well as from real traffic information, such that predicting the mobility pattern of the UEs on highways with or without accidents. [0033] FIG. 4 illustrates the signaling flow utilized for implementing the flow control mechanism in a LTE network in accordance with an example implementation. [0034] PCEF collects information from the following sources. The PCRF provides the UE subscription information and thus the QoS class of all connected UEs 401. The traffic prediction module provides the likely traffic pattern in near future at source and target eNBs for a geographical area served by the source and target eNBs 402. The mobility prediction module provides the mobility prediction (e.g., likely path) of the mobile UEs in the near future for the UEs in the tracking/geographical area 403. The source eNB provides served UE-specific RAN information such as spectral efficiency, average number of physical resource blocks (PRBs) used, pathloss, long term CQIs, PER, mobility information, and so on depending on the desired implementation 404. The target eNB provides served UE-specific RAN information such as spectral efficiency, average number of PRBs used, pathloss, long term CQIs, PER, mobility information, and so on, depending on the desired implementation 405. [0035] At 406, based on the above information, the PCEF determines whether RAN congestion exists or is likely to exist in the near future in the target eNB predicted to receive moving UEs. The PCEF applies UE-specific anticipatory flow control for the downlink traffic likely to be handed over to target eNB at source eNB, if source eNB has sufficient resources. In the event of congestion, PCEF sends a signal to the video server such that the video flow rates (e.g., segmentation) are adjusted for a selected subset of mobile UEs. At 407, the PCEF conducts setup of the data bearer for the downlink traffic with the appropriate flow control. [0036] FIG. 5 shows the operation at the video server or the content data network (CDN) cache, in accordance with an example implementation. At first, the video server or CDN obtains feedback about the buffer state of the client 501. Then, when the server obtains a trigger from PCEF for refragmenting packets 502, a check is performed at 503 to determine if the trigger indicates congestion at the target eNB for a particular UE. If not (e.g., no), the server proceeds with adaptive streaming utilizing uniform segmentation at 505. Otherwise (e.g., yes) the server increases the segments times such that more information is buffered at the client to avoid buffer underrun by decreasing throughput when the UE is handed over to target eNB at 504. Increasing the segmentation times for the adaptive video streaming, such that instead of dividing the media file into video segments of equal size (e.g. segments of length 2s or length 10s), the video segment sizes can be momentarily increased (e.g. to 20s), such that more information is pre-fetched to fill the client buffer before handing over the user to a congested target eNB. The variable size of the video segments (e.g. 20s) is determined according to the level of congestion and the amount of resources available to accommodate the UE that is chosen for flow control. The video segments sizes can be decreased to a predetermined fixed size after flow control (e.g. uniform size of 2s). The operations at the video client are configured to playback the variable segments. As illustrated in FIG. 5, if the trigger does not indicate congestion, the server proceeds with uniform segmentation of the video files. The PCEF flow control flag or trigger for the UEs can be based on their QoS class, current flow rates, current and future congestion level, RAN statistics and velocity. [0037] FIG. 6 illustrates the hardware components of the video server, in accordance with an example implementation. Video server can include a processor 601, a storage 602, a memory 603 and input/output interfaces 604. Video server can be in the form of any apparatus that can facilitate the functionality of a video server. Memory 603 may be configured to store user equipment (UE) information regarding buffer status of a UE that is to undergo handover from a first base station to a second base station, and store variably sized segment flags associated with UE from the PCEF as illustrated in FIG. 5. Storage 602 may buffer segments from the internet which are sent to the corresponding PCEF by the processor 601 based on the flow diagram of FIG. 5. In an example, processor may, for the variably sized segment flag indicating provision of variably sized segments for the UE, provide variably sized segments to the UE and for the variably sized segment flag not indicating the provision of the variably sized segments, provide uniformly sized segments to the UE as illustrated in FIG. 5. As described above in example implementations, the provision of variably sized segments to the UE may involve increasing size of the segments provided to the UE. [0038] The video segments size (uniform) can be determined by the adaptive streaming algorithm when dividing the video into multiple segments. The size can then be increased based on the variably sized segment flag taking into account the traffic load, the current application rate of the UE, the velocity and the QoS class of the UE for example. The sizes can be chosen from a set number of values such as a lookup table in an example implementation. Table 1 illustrates an example lookup table and adjustment to segment size (e.g., +1 to length) based on network, UE, and application characteristics. [0039] Table 1: Example segmentation modification lookup table

[0040] The example provided in Table 1 is not intended to be limiting, and may be adjusted to reflect other characteristics or adjustments to the segment size according to the desired implementation. [0041] FIG. 7 shows the detailed function diagram of the flow control mechanism, in accordance with an example implementation. The mobility prediction module and the traffic prediction module may be used to determine the mobility pattern and the congestion level at the source and target eNBs. In the event of identified RAN congestion at the target eNB, K mobile UEs to be handed over to target eNB are identified for flow control. Out of these K UEs, M video users are identified for adaptive video streaming variable segmentation. [0042] To further determine which of the M UEs receives priority for flow control and increased RAN resources, these M UEs can be ordered according to their QoS classes and/or velocities (e.g., train users, car users, pedestrians, etc.). The UEs with the highest QoS class and worst expected radio conditions (e.g., fastest UEs) are prioritized for variable video segmentation and hence higher flow rate control. Scheduling these UEs for anticipative adaptive video streaming incorporates a scheduling algorithm where priority is provided for these UEs over other UEs with delay-tolerant applications or stationary UEs in the source cell. [0043] At 700, the flow control mechanism obtains information about mobility pattern and parameters of the users in the source eNB cell. At 701, the flow control mechanism obtains information about predicted traffic congestion at source and target eNBs. At 702, the flow control mechanism determines the current download traffic served by the source eNB. At 703, the flow control mechanism determines if the target cell is currently^ congested or likely to be congested in immediate future. If not (NO), the flow ends, otherwise (YES) the flow proceeds to 704. [0044] At 704, the flow control mechanism determines a subset of K UEs with moving towards target eNB cell. At 705, the flow control mechanism then determines a subset M of K users that are video users. At 706, the flow control mechanism pre-fetches video content for moving users. [0045] FIG.8 illustrates example hardware configurations for a PCEF, in accordance with an example implementation. In FIG.8, there is a motherboard 800 having a random access memory (RAM) 801 and central processing unit (CPU) 802, storage 803 and network interface 804. Network interface 804 can be configured to communicate with the internet and other elements of the EPC. Storage 803 may be configured with instructions to facilitate the functionality of the PCEF, which is loaded into memory 801 and executed by CPU 802. Note that PCEF and P-GW functionality can be combined into single hardware device. For example, P-GW functionality can also be stored into storage 803, and executed by CPU 802 when loaded into memory 801. [0046] Table 1 shows an example of how example implementations can take into account the velocity of the UEs and their application as criteria. Assumptions include a source eNB with attached UEs that are either stationary or mobile, with some of the UEs expected to be handed over to a neighboring target eNB with high network utilization, such that congestion level is high, and the handed over UEs will not be immediately scheduled risking a drop in their QoE. The example implementation identifies target UEs (UE-1, UE-3, UE-6) for variable video segmentation based on their velocity, QoS class, application (video). Next, the UEs are selected based on in the flow control mechanism in FIG.7, and variable video segmentation is performed as shown in FIG.5 and Table 2. The size of the video segments can be chosen based on current application rate of the UE, level of congestion at the target base station, velocity and QoS class of the UEs. Table 2: An example illustrating variable segmentation for

UEs with varying velocity and application type

[0047] For the case when optimization for flow control can be performed at both the source and target eNB, the anticipated video streaming with variable segmentation can be compensated for predictive flow control at the target eNB. In the latter case, when the congestion level is determined to be high or predicted to be high in the near future, the flow control mechanism is performed by the PCEF to decrease the video rates and release RAN resources to relieve congestion. [0048] FIG. 9 illustrates a flow diagram of an example implementation for an examplary flow control mechanism where both the base stations (source eNB, and target eNB) are involved in the congestion control process, in accordance with an example implementation. [0049] At 900, the flow control mechanism obtains information about mobility pattern and parameters of the users in the source eNB cell. At 901, the flow control mechanism obtains information about predicted traffic congestion at source and target eNBs. At 902, the flow control mechanism determines the current download traffic served by the source eNB. At 903, the flow control mechanism determines if the target cell is currently^ congested or likely to be congested in an immediate future (e.g., a few minutes before handover, initiation of handover procedures, or other time period defined by the operator). If not (NO), the flow ends, otherwise (YES) the flow proceeds to 904. [0050] At 904, the flow control mechanism determines a subset of K UEs with moving towards target eNB cell. At 905, the flow control mechanism then determines a subset M of K users that are video users. At 906, the flow control mechanism can then pre-fetch video content for moving users M’ < M at source eNB as well as apply predictive flow control according to QoS classes and RAN usage for V users at target eNB at 907. [0051] According to another flow control mechanism, whereas a third neighboring base station is involved in the optimization to relieve congestion. Assume a neighboring base station to the source eNB such that the neighboring base station is not congested and some of the traffic from the source eNB can be offloaded to the neighboring base station on demand. In this example, the traffic for some of the UEs in source base station can be offloaded to the neighboring base station such that the radio quality from the neighboring base station to offloaded UEs is satisfactory for the UEs not to lose their QoS or QoE. This makes resources available at the source eNB to increase the flow control for video users anticipated to be moving to congested target eNB. [0052] FIG. 10 shows the flowchart diagram of the neighboring base station case, in accordance with an example implementation. At 1000, the flow control mechanism obtains information about the mobility pattern and parameters of the users in the source eNB cell. At 1001, the flow control mechanism obtains information about predicted traffic congestion at the source and target eNBs. At 1002, the flow control mechanism determines the current downlink traffic served by the source eNB and the load at neighboring base station through resource report request. [0053] At 1003, the flow control mechanism determines if the target cell is currently congested or likely to be congested in immediate future. If not (NO), the the flow ends, otherwise (YES), the flow proceeds to 1004. [0054] At 1004, the flow control mechanism determines a subset of K UEs moving towards the target eNB cell and a subset of L UEs that can be offloaded to the neighboring eNB 1004. At 1005, the flow control mechanism determines a subset M of K Users that are video users. [0055] Table 2 shows an example implementation involving source eNB, target eNB and neighboring eNB, and example UEs {UE-1, UE-2, UE-3, UE-4, UE-5, UE-6} and their application as criteria. Assumptions include all example UEs served by source eNB, example UEs are either stationary or mobile, with a mobility pattern such that UE-1 and UE-3 are expected to be handed over to target eNB. Target eNB is predicted to be congested in the near future. UE-2 and UE-5 are located such that their measurement reports indicate close proximity with neighboring eNB, and their traffic is a non-video traffic. [0056] The example implementation identifies target UEs (UE-1, UE-3) for variable video segmentation based on their velocity and their mobility pattern, and their application (video), and identifies UEs (UE-2, UE-5) to be handed over to neighboring eNB such that the handover trigger threshold is changed or load is exchanged between source eNB and neighboring eNB over an X2 interface taking into account the traffic in source eNB and neighboring eNB. The size of the video segments for UE-1 and UE-3 is increased based on their current application rate, level of congestion at target eNB, their velocity, and the amount of PRBs that are made available by offloading UE-2 and UE-5 to BS_m. Table 2: table showing example numbers for UEs handed over between BS_s, BS_t,

BS_m

[0057] FIG. 11 illustrates an example computing environment with an example computer device suitable for use in some example implementations, such as an EPC apparatus to facilitate the functionality of the EPC or one or more elements of the EPC. Alternatively, elements such as the PCEF and P-GW can be implemented in a switch configuration as illustrated in FIG. 8 and communicatively coupled to the computing environment as illustrated in FIG. 11. Computer device 1105 in computing environment 1100 can include one or more processing units, cores, or processors 1110, memory 1115 (e.g., RAM, ROM, and/or the like), internal storage 1120 (e.g., magnetic, optical, solid state storage, and/or organic), and/or I/O interface 1125, any of which can be coupled on a communication mechanism or bus 1130 for communicating information or embedded in the computer device 1105. [0058] Computer device 1105 can be communicatively coupled to input/user interface 1135 and output device/interface 1140. Either one or both of input/user interface 1135 and output device/interface 1140 can be a wired or wireless interface and can be detachable. Input/user interface 1135 may include any device, component, sensor, or interface, physical or virtual, that can be used to provide input (e.g., buttons, touch-screen interface, keyboard, a pointing/cursor control, microphone, camera, braille, motion sensor, optical reader, and/or the like). Output device/interface 1140 may include a display, television, monitor, printer, speaker, braille, or the like. In some example implementations, input/user interface 1135 and output device/interface 1140 can be embedded with or physically coupled to the computer device 1105. In other example implementations, other computer devices may function as or provide the functions of input/user interface 1135 and output device/interface 1140 for a computer device 1105. [0059] Examples of computer device 1105 may include, but are not limited to, highly mobile devices (e.g., smartphones, devices in vehicles and other machines, devices carried by humans and animals, and the like), mobile devices (e.g., tablets, notebooks, laptops, personal computers, portable televisions, radios, and the like), and devices not designed for mobility (e.g., desktop computers, other computers, information kiosks, televisions with one or more processors embedded therein and/or coupled thereto, radios, and the like). [0060] Computer device 1105 can be communicatively coupled (e.g., via I/O interface 1125) to external storage 1145 and network 1150 for communicating with any number of networked components, devices, and systems, including one or more computer devices of the same or different configuration. Computer device 1105 or any connected computer device can be functioning as, providing services of, or referred to as a server, client, thin server, general machine, special-purpose machine, or another label. [0061] I/O interface 1125 can include, but is not limited to, wired and/or wireless interfaces using any communication or I/O protocols or standards (e.g., Ethernet, 802.11x, Universal System Bus, WiMax, modem, a cellular network protocol, and the like) for communicating information to and/or from at least all the connected components, devices, and network in computing environment 1100. Network 1150 can be any network or combination of networks (e.g., the Internet, local area network, wide area network, a telephonic network, a cellular network, satellite network, and the like). [0062] Computer device 1105 can use and/or communicate using computer-usable or computer-readable media, including transitory media and non-transitory media. Transitory media include transmission media (e.g., metal cables, fiber optics), signals, carrier waves, and the like. Non-transitory media include magnetic media (e.g., disks and tapes), optical media (e.g., CD ROM, digital video disks, Blu-ray disks), solid state media (e.g., RAM, ROM, flash memory, solid-state storage), and other non-volatile storage or memory. [0063] Computer device 1105 can be used to implement techniques, methods, applications, processes, or computer-executable instructions in some example computing environments. Computer-executable instructions can be retrieved from transitory media, and stored on and retrieved from non-transitory media. The executable instructions can originate from one or more of any programming, scripting, and machine languages (e.g., C, C++, C#, Java, Visual Basic, Python, Perl, JavaScript, and others). [0064] Processor(s) 1110 can execute under any operating system (OS) (not shown), in a native or virtual environment. One or more applications can be deployed that include logic unit 1160, application programming interface (API) unit 1165, input unit 1170, output unit 1175, and inter-unit communication mechanism 1195 for the different units to communicate with each other, with the OS, and with other applications (not shown). The described units and elements can be varied in design, function, configuration, or implementation and are not limited to the descriptions provided. [0065] In some example implementations, when information or an execution instruction is received by API unit 1165, it may be communicated to one or more other units (e.g., logic unit 1160, input unit 1170, output unit 1175). In some instances, logic unit 1160 may be configured to control the information flow among the units and direct the services provided by API unit 1165, input unit 1170, output unit 1175, in some example implementations described above. For example, the flow of one or more processes or implementations may be controlled by logic unit 1160 alone or in conjunction with API unit 1165. The input unit 1170 may be configured to obtain input for the calculations described in the example implementations, and the output unit 1175 may be configured to provide output based on the calculations described in example implementations. [0066] I/O interface 1125 may be configured to receive information associated with a RAN and to communicate with the RAN, including the eNodeBs and associated UEs. Memory 1115 is configured to store information relating the one or more UEs to one or more RAN related metrics based on the information received through the I/O interface 1125. [0067] As described with respect to FIG. 4, memory 1115 may be configured to store user equipment (UE) information regarding a mobility pattern of a plurality of UEs associated with a source base station as received from mobility prediction, predicted traffic congestion at the source base station and a target base station as received from traffic prediction, and a radio access network (RAN) traffic report associated with the source base station and the target base station. [0068] Processor(s) 1110 are configured to, based on the predicted traffic congestion and the RAN traffic report associated with the second base station, determine whether the second base station is congested or predicted to be congested within a time period as illustrated in FIGS. 4, 7, 9 and 10. Time period may be adjusted by the operator of the PCEF or EPC to account for a desired threshold for offloading a UE to the target base station, or other desired implementations. For a determination that the target base station is congested or predicted to be congested within the time period, the processor(s) 1110 can determine moving ones of the plurality of UEs associated with the source station that are moving to the target base station based on the mobility pattern; determine video users of the moving ones of the plurality of UEs associated with the source station that are moving to the target base station based on downlink traffic; and apply flow control on video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the source station that are moving to the target base station as illustrated in FIGS. 4, 7, 9 and 10. For a determination that the target base station is not congested or predicted to be not congested within the time period, processor(s) 1110 maintain transmission of uniform segments to the video users. [0069] Further, processor(s) 1110 can be configured to apply flow control on the video to be transmitted to the video users by pre-fetching segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station; and determining adjustments to the size of the segments to be transmitted as segments are transmitted to the video users of the moving ones of the plurality of UEs associated with the source base station that are moving to the target base station as illustrated in FIGS. 7, 9 and 10. For the determination that the target base station is congested or predicted to be congested within the time period, the processor(s) are configured to adjust RAN resource usage for one or more UEs associated with the target base station as illustrated in FIGS.7, 9 and 10. [0070] Processor(s) 1110 can also, for the determination that the target base station is congested or predicted to be congested within the time period, determine a subset of the video users of the moving ones of the plurality of UEs associated with the source base station that are moving to the target base station that can be offloaded to a neighboring base station to the source base station, and offload the subset of the video users of the moving ones of the plurality of UEs associated with the source base station that are moving to the target base station, to the neighboring base station. Information regarding neighboring base stations, or base stations within a coordinated multipoint (CoMP) set may be managed in memory 1115 to determine the identities of neighboring base stations. [0071] Processor(s) 1110 are configured to pre-fetch segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the source base station that are moving to the target base station by transmitting an indication indicative of the determination that the target base station is congested or predicted to be congested within the time period to a video server corresponding to the segments. The indication can be in the form of a variably sized flag to be processed by the video server as illustrated in FIG.5. [0072] FIG. 12 illustrates an example base station upon which example implementations can be implemented. The block diagram of a base station 1200 in the RAN of the example implementations is shown in FIG. 12, which could be a macro base station, a pico base station, an eNB and so forth. The base station 1200 may include the following modules: the Central Processing Unit (CPU) 1201, the baseband processor 1202, the transmission/receiving (Tx/Rx) array 1203, the X2/Xn, S1-MME, and S1-U interfaces 1204, and the memory 1205. The CPU 1201 is configured to execute one or more modules or flows as described, for example, in FIG. 4 to provide UE RAN information to the EPC apparatus. [0073] The baseband processor 1202 generates baseband signaling including the reference signal and the system information such as the cell-ID information. The Tx/Rx array 1203 contains an array of antennas which are configured to facilitate communications with associated UEs. The antennas may be grouped arbitrarily to form one or more active antenna ports. Associated UEs may communicate with the Tx/Rx array to transmit signals containing congestion information, flow traffic information, and so forth. The X2/Xn interface 1204 is used to exchange traffic and interference information between one or more base stations, and S1-MME & S1-U interfaces are used to exchange information with the EPC apparatus to transmit instructions for UE flow traffic and UE mobility as described above. The memory 1205 can be configured to store and manage traffic information, traffic load, and so forth. Memory 1205 may take the form of a computer readable storage medium or can be replaced with a computer readable signal medium as described below. [0074] FIG. 13 illustrates an example user equipment upon which example implementations can be implemented. The UE 1300 may involve the following modules: the CPU module 1301, the Tx/Rx array 1302, the baseband processor 1303, and the memory 1304. The CPU module 1301 can be configured to perform one or more functions, such as execution of the flows or modules as described, for example, in FIG. 4 to have flow control imported onto the UE by the data bearer. The Tx/RX array 1302 may be implemented as an array of one or more antennas to communicate with the one or more base stations. The memory 1304 can be configured to store congestion information and flow traffic. The baseband digital signal processing (DSP) module can be configured to perform one or more functions, such as to conduct measurements to generate the position reference signal for the serving base station to estimate the location of the UE. [0075] Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to convey the essence of their innovations to others skilled in the art. An algorithm is a series of defined steps leading to a desired end state or result. In example implementations, the steps carried out require physical manipulations of tangible quantities for achieving a tangible result. [0076] Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,”“computing,”“calculating,”“determining,”“displaying,” or the like, can include the actions and processes of a computer system or other information processing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system’s registers and memories into other data similarly represented as physical quantities within the computer system’s memories or registers or other information storage, transmission or display devices. [0077] Example implementations may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer readable medium, such as a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation. [0078] Various general-purpose systems may be used with programs and modules in accordance with the examples herein, or it may prove convenient to construct a more specialized apparatus to perform desired method steps. In addition, the example implementations are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the example implementations as described herein. The instructions of the programming language(s) may be executed by one or more processing devices, e.g., central processing units (CPUs), processors, or controllers. [0079] As is known by those skilled in the art, the operations described above can be performed by hardware, software, or some combination of software and hardware. Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application. Further, some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software. Moreover, the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format. [0080] Moreover, other implementations of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings of the present application. Various aspects and/or components of the described example implementations may be used singly or in any combination. It is intended that the specification and example implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims.

Claims

CLAIMS What is claimed is: 1. An apparatus, comprising: a memory, configured to store user equipment (UE) information regarding a mobility pattern of a plurality of UEs associated with a first base station, predicted traffic congestion at the first base station and a second base station, and a radio access network (RAN) traffic report associated with the second base station; a processor, configured to: based on the predicted traffic congestion at the first base station and the second base station, and the RAN traffic report associated with the second base station, determine whether the second base station is congested or is predicted to be congested within a time period; for a determination that the second base station is congested or is predicted to be congested within the time period: determine moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on the mobility pattern; determine video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on downlink traffic; and apply flow control on video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. 2. The apparatus of claim 1, wherein the processor is configured to apply flow control on the video to be transmitted to the video users by: pre-fetching segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station; and determining adjustments to the size of the segments to be transmitted as segments are transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. 3. The apparatus of claim 2, wherein, for the determination that the second base station is congested or is predicted to be congested within the time period, the processor is further configured to adjust RAN resource usage for one or more UEs associated with the second base station. 4. The apparatus of claim 2, wherein, for the determination that the second base station is congested or is predicted to be congested within the time period: determine a subset of the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station that can be offloaded to a third base station, the third base station being a neighboring base station to the first base station; and offload the subset of the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station, to the third base station. 5. The apparatus of claim 2, wherein the processor is configured to pre-fetch segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station by transmitting an indication indicative of the determination that the second base station is congested or predicted to be congested within the time period to a video server corresponding to the segments. 6. The apparatus of claim 1, wherein the processor is configured to, for a determination that the second base station is not congested or is predicted to be not congested within the time period, maintain transmission of uniform segments to the video users. 7. A method, comprising: managing user equipment (UE) information regarding a mobility pattern of a plurality of UEs associated with a first base station, predicted traffic congestion at the first base station and a second base station, and a radio access network (RAN) traffic report associated with the second base station; based on the predicted traffic congestion at the first base station and the second base station, and the RAN traffic report associated with the second base station, determining whether the second base station is congested or is predicted to be congested within a time period; for a determination that the second base station is congested or is predicted to be congested within the time period: determining moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on the mobility pattern; determining video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station based on downlink traffic; and applying flow control on video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. 8. The method of claim 7, wherein the applying flow control on the video to be transmitted to the video users comprises: pre-fetching segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station; and determining adjustments to the size of the segments to be transmitted as segments are transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station. 9. The method of claim 8, further comprising, for the determination that the second base station is congested or is predicted to be congested within the time period, adjusting RAN resource usage for one or more UEs associated with the second base station. 10. The method of claim 8, further comprising, for the determination that the second base station is congested or is predicted to be congested within the time period: determining a subset of the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station that can be offloaded to a third base station, the third base station being a neighboring base station to the first base station; and offloading the subset of the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station, to the third base station. 11. The method of claim 8, wherein the pre-fetching segments of the video to be transmitted to the video users of the moving ones of the plurality of UEs associated with the first base station that are moving to the second base station comprises transmitting an indication indicative of the determination that the second base station is congested or predicted to be congested within the time period to a video server corresponding to the segments. 12. The method of claim 7, wherein, for a determination that the second base station is not congested or predicted to be not congested within the time period, maintain transmission of uniform segments to the video users. 13. An apparatus, comprising: a memory, configured to store user equipment (UE) information regarding buffer status of a UE that is to undergo handover from a first base station to a second base station, and a variably sized segment flag associated with UE; and a processor, configured to: for the variably sized segment flag indicating provision of variably sized segments for the UE, provide variably sized segments to the UE. 14. The apparatus of claim 13, wherein the processor is configured to, for the variably sized segment flag not indicating the provision of the variably sized segments, provide uniformly sized segments to the UE. 15. The apparatus of claim 13, wherein the processor is configured to provide variably sized segments to the UE by increasing size of the segments provided to the UE.